XXVI Brazilian Congress on Biomedical Engineering

The World Health Organization has estimated 14.1 million new cases of cancer in 2012 and 8.8 million deaths in 2015. Lung cancer being the most fatal and prevalent of all. Therefore it is important for public health to use means that provide faster and more accurate diagnoses. Computer Assisted Diagnosis systems (CADs) have the potential to improve the accuracy of imaging diagnoses and are effective in compensating for the deficiency in the performance of the human eye in the detection of minor lesions. Mainly, when lung segmentation fails to delineate the lungs correctly, nodules may be lost. This work proposes an approach with 3D connected components analysis for lung segmentation. The results shows very little loss in the number of nodules lost with only 1.9% of 2663 nodules present in the dataset, in an average time of 19.3 s per exam, compared to 223 s of the 3D region growing method.

Computer Aided Diagnosis and Visualization have been widely used in several medical fields. In cardiology, there are currently two techniques used in the measurement of myocardial deformation in two dimensions: Tissue Doppler and 2D Strain. The first technique has drawbacks regarding the dependence on the angle of insonation of the transducer during the echocardiographic examination. The second technique, Speckle Tracking, consists of tracking the natural acoustic markers in the image produced by ultrasound. In this paper we perform a comparative analysis of time-delay estimators applied in synthetic and medical videos from echocardiographic examinations. The evaluation of the techniques is performed on the deformation (strain curves and strain rate) of phantoms and echocardiographic images and the results prove to be feasible to track speckle marks during the cardiac cycle.

A Brain-Computer Interface (BCI) is a direct communication pathway between the human brain and an external device or machine. Those systems can be controlled by invasive or non-invasive brain signals. Examples of non-invasive systems are Electroencephalography-based (EEG) BCIs for Motor Imagery (MI) detection. Field Programmable Gate Arrays (FPGAs) could be used in online BCIs for parallel computation purposes. In this work, an FPGA-based BCI was developed in order to decompose a raw EEG signal into its different types of rhythms such as beta (β), alpha (α), theta (θ), and delta (δ), by using filter banks based on the Daubechies-4 Discrete Wavelet Transform (DWT). The designed system generates all coefficients in real-time and uses both serial and video communication interfaces for visualization and analysis purposes. The input signals, used for testing, came from an open source database. For validation purposes, an off-line signal processing confirmed the accuracy of results. The outcome showed that the use of multi-rate filters resulted in low hardware resources consumption.

Electroencephalography (EEG) is a noninvasive technique that acquires signals from the scalp triggered by brain electrical activities. Through this technique, it is possible to develop real-time Brain-Computer Interfaces (BCIs) that are able to control cyber-mechanical devices. In addition, the Discrete Wavelet Transform (DWT) is a signal processing tool that decomposes an EEG input signal vector into its sub-bands beta, alpha, theta, and delta. In this work, by using an algorithm based on the DWT and filter banks, the alpha sub-band could be extracted from a raw EEG signal, thus enabling the calculation of its power. By utilizing this methodology, it was possible to measure the Event-Related Desynchronization (ERD) and Event-Related Synchronization (ERS), in order to develop a synchronous EEG-based BCI for hand Motor Imagery (MI) detection. A device synthesized in FPGA was developed to calculate the power spectrum of the EEG alpha rhythms from C3 and C4 channels in real-time, aiming the feeding of a classifier circuit block that labels the MI as a left-hand or right-hand class of movement. The novelty of this work mainly consists of the development of an IP core for real-time parallel calculation of ERD. The main motivation of this work is providing a control tool for robotic arms or virtual reality devices by using real-time MI recognition.

Atherosclerosis is a major cause of acute coronary events. The development of methods capable of identifying and classifying the mechanical properties of atherosclerotic plaques is of great interest in the medical field. In order to properly identify the plaques, it is first necessary to accurately track its deformation under different pressures. The aim of this study is to, first, propose a pixel tracking methodology for application in intravascular ultrasound images, and second, evaluate the pixel tracking method through a novel automated method. The framework proposed utilizes a numerical phantom of the artery and mechanically deforms an atherosclerotic plaque by applying different intraluminal pressures, providing a known position map of control points on the plaque throughout the deformation of the tissue. We track the position of the control points through two subsequential frames that correspond to diastolic and systolic intraluminal pressures. We evaluate the accuracy of the pixel tracking method by comparing the tracked positions of the control points with the known positions of the control points through statistical analysis. This study has the potential to provide a new metrics for evaluating distinct pixel tracking parameters and methods, contributing to provide a safer, more accurate method of evaluating atherosclerotic plaques.

After a stroke, the hand grip movement of the hemiparetic hand is impaired due to the loss of corticospinal input to the common drive. The analysis of the muscles coupling during movements can be made using the coherence method. This study proposed the analysis of the Magnitude Squared Coherence (MSC) between agonist-agonist (AG-AG) and agonist-antagonist (AG-AN) muscle pairs in a group of 9 post stroke participants compared to 32 healthy controls. Surface Electromyographic signals were acquired from the muscles Extensor Digitorum, Extensor Pollicis Brevis, Flexor Digitorum Superficialis, Flexor Digitorum Profundus and Flexor Pollicis Longus during hand grip movement and rest. The MSCs from the two participant groups were compared in the frequency bands delta, theta, alpha, beta and gamma, using a Wilcoxon test for independent samples. The results showed that the MSCs of AG-AG and AG-AN muscle pairs in post stroke participants are smaller (p < 0.01) than in healthy controls during hand grip movement. At rest, the behavior is repeated for the AG-AG muscles, but there is more coupling between AG-AN muscle pairs in post stroke participants in the lower frequencies. This can indicate that the coupling between AG-AG and AG-AN muscles is weakened in post Stroke participants and that there are anomalous motor unit activations at rest in AG-AN muscle pairs. This can happen due to the damaged corticospinal inputs to the common drive in post stroke participants.

The quasi-static elastography is a technique used to generate real-time images of elastic properties of the biological tissue. However, quasi-static mode is operator-dependent, which may generate limitations in its reproducibility and reliability. The purposes of this study were to compare the elasticity index (E) between two different muscle regions (superficial and deep) and two probe positions (longitudinally and transversally to fibers) of a piece of fresh bovine beef and to investigate the intra-rater reliability of the E measurements. E values obtained moderate reliability (CV = 9.64 to 22.17%; ICC = 0.00 to 0.83). E values for region of interest (ROI) deep were higher (i.e., stiffer) than ROI superficial (p < 0.001) for both probe positions. Anisotropic effect was confirmed by higher E values (1.88 ± 0.16) longitudinally (for the deep ROI) and transversally (for the superficial ROI) (E = 0.90 ± 0.06) to the fibers. It is recommended to position the probe longitudinally to the fibers adopting a superficial or deep ROI (≈10 mm of surface).

Electroencephalography (EEG) inverse problem intends to determine the internal brain signals from EEG electrode signals. Being an ill-posed problem, it needs a regularization function and a regularization parameter (λ). Many techniques have been proposed with different regularization functions, yet all of them still depends on the regularization parameter that can drastically change the solution given its value. In this study we intend to determine if the regularization parameter can be set to a specific value or a smaller interval, and if not, discover what changes in the problem settings causes it to vary. For that we simulated source signals and calculated the signals that these would generate in the EEG electrodes. We then used a genetic algorithm to find the best value of λ applied in different experiments having different regularization functions. Further study is still required, but we could conclude that, for the settings used in this study, different techniques demand different values of λ, and for some experiments with different source signals the value of λ maintained in a smaller interval.

This work is designed to assist doctors in diagnosing Alzheimer’s disease (AD). The proposal consists in the creation of an artificial intelligence, a study and a series of tests, and a solution for the submission of a human being or an AD holder. Through graphs based on the probability of the response obtained from the program User know the correct answer with precision and details. The resonance classifier was built using a Google TensorFlow API on the Git Bash virtual machine, emulating the Linux operating system, with analytical data from the mobile-net algorithm. It was prepared for the Machine Learning process, using magnetic resonance imaging of patients with AD and healthy. The project aims to diagnose the disease before them. Thus, medication is easier, in addition to preparing the patient and his family for their future situation. The results of the tests prove a viability of the system, since it activates only 9.21% of error, beating experts in Alzheimer’s.

The vectorcardiogram (VCG) is an useful method for automatic prognosis and diagnosis in different medical scenarios. However, its method of acquisition presents some disadvantages related to complexity of the standard system proposed by Ernest Frank. For this reason, Jan A. Kors proposed a method of reconstructing the VCG from the usual ECG leads. This paper studied the validation of the Kors’s technique by comparing the VCG plotted-by-plane images using two metrics of image processing. The group of healthy subjects presented the best results with mean PSNR of 18.137 dB and mean SSIM of 90.5% for VCG XY plane images.

In several applications involving medical image analysis, the process of image segmentation, be it automatic or manual, is a present task. An accurate segmentation provides information for inspection of anatomical structures, to identify diseases and monitoring of its progress, and even for surgical planning and simulation. Thus, the role of image segmentation is essential in any medical image analysis system. Among the segmentation techniques in the literature, the active models technique is one of the most popular approaches of the last two decades and has been widely used in medical image segmentation, achieving considerable success. Active models that are applied on three-dimensional applications are called Active Surfaces Methods (ASM), which has been widely used in the segmentation of 3D objects, evolving under the influence of their energy to converge to the desired surface. So, knowing how essential surface extraction is to obtain an accurate segmentation, this paper proposes an automatic initialization method for ASM applied in lobes segmentation in CT images. The proposed method has achieved significant results, with overall accuracy rate of 94%.

Pulmonary diseases are the cause of millions of deaths around the world. Chest radiography is one of the most used medical imaging modality for the identification and diagnosis of pulmonary abnormalities. However, segmentation of the pulmonary region is important to obtain objective information and extract characteristics necessary for the diagnosis. In the literature are available some pulmonary segmentation methods based on different processing techniques. However, these still have some limitations. Consequently, we present an automatic pulmonary segmentation approach, based on Enhancement by Wavelet, Mathematical Morphology and Active Contour. The methodology is divided into three steps. First, the original image is enhanced by discrete wavelet. Next, occurs a combination of the Otsu threshold followed by a series of morphological operations to identify the pulmonary object; hence, pulmonary tissue information is discriminated and binarized. Finally, Active Contour method improves the binary information of pulmonary tissue, mainly in the border region. The evaluation was performed in 247 digital chest X-ray images of human. The high accuracy and robustness of the proposed method was demonstrated with values of Overlap (OR (%)) = 95.9 ± 2.9, and Average Contour Distance (ACD (mm)) = 0.76 ± 0.92, better than currently literature results.

The skull stripping procedure is an important image preprocessing step commonly applied in many neuroscience studies. Even though several efforts have been made in order to create robust brain extraction algorithms, minor segmentation errors still remain, often requiring manual refinement. In this study, an automatic Brain Volume Refinement (BVeR) method is proposed. The method interprets segmentation outliers as local interference in brain tissue signal contrast, offering a suitable solution for external brain boundary adjustment of structural T1 and T2 weighted MRI. Two publicly available structural MRI image datasets of healthy adults and two commonly used brain extraction methods (BET and FreeSurfer) were used for evaluation. Quantitative segmentation evaluation for accuracy and reproducibility were applied to evaluate the performance of BVeR, showing that the average brain volume refinement showed a significant improvement (p < 0.001) in most metrics. In conclusion, the BVeR method offers an automatic alternative to the manual correction often requested in brain MRI studies, in which it considerably reduces human errors and processing time.

Recently, deep learning has shown great success in many computer vision applications. The ability to learn image features and use these features for object localization, classification and segmentation has paved the way for new medical image studies, improving the performance of automated computer-aided detection (CADe) systems. In this paper, a new approach is proposed for classification of breast tumors in ultrasound (US) images, based on convolutional neural networks (CNN). The database consists of 641 images, histopathologically classified in two categories (413 benign and 228 malignant lesions). To have a better estimate of model’s classification performance, the data were split to perform fivefold cross validation. For each fold, 80% of data was used for training, and 20% for the evaluation. Different evaluation metrics were used as performance measurements. With the proposed network architecture, we achieved an overall accuracy of 86.12% for tumor classification and the area under the ROC curve (AUC) equal to 0.934. After applying image augmentation and regularization, the accuracy and the AUC increased to 92.01% and 0.9716%, respectively. The obtained results surpassed other machine learning methods based on manual feature selection, demonstrating the effectiveness of the proposed method for the classification of tumors in US imaging.

Marmosets are highly vocal, social primates, which place them in a leading position for studies comprising the auditory system. Single-neuron recordings have previously described a tonotopic organization in marmoset A1. However, it is still unknown how the processing of auditory stimuli of different frequencies is reflected in the Local Field Potential (LFP) of marmoset’s A1. In this work, we address this issue by assessing sound frequency tuning in marmoset A1 using auditory evoked potentials (AEP) extracted from LFPs. We found that the amplitude and latency of AEPs for different frequencies reflect previously reported behavioral audiograms. Our results reinforce the AEP as an electrophysiological signature of both cortical auditory processing and animal behavioral outcomes.

Heart failure is a common syndrome that is associated with changes in autonomic activity. The analysis of heart rate variability provides information about autonomic status. There are few data on the circadian behavior of HRV in patients with heart failure, especially in the acutely decompensated. The present study aims at comparing the parameters obtained from HRV in the daytime and nighttime periods. Patients admitted to an intensive care unit with a diagnosis of decompensated heart failure and who performed 24 h-Holter at the unit were selected. All patients underwent an echocardiogram on admission. The left ventricular ejection fraction (LVEF) was used to separate the patients in the HFrEF (LVEF < 50%) and HFpEF (LVEF ≥ 50%) groups. Eighty-two Holter registries were identified. Two patients were excluded because they were pacemaker users, three by non-sinus rhythm, three by ventricular instability and 15 by intense noise in the signal, totaling 59 patients for analysis. In the general population, occurred reduced HRV levels, however with no difference between the day and night periods. Among patients with HFpEF, there was a tendency to reduce mean HR at night but this trend did not reach statistical significance. Among the patients with low mean HR, a HR reduction was observed at night, but no difference was found in the other parameters. In the patients with elevated HR, no difference was found in the daytime and nighttime periods. In conclusion, patients admitted with decompensated heart failure had reduced HRV levels, particularly in patients with HFrEF. However, no changes were found in the evaluation of the parameters during the day and night periods.

The electroencephalogram (EEG) has been used to control non-invasive brain-computer interface (BCI). EEG Signal is very susceptible to artifact that can interfere on the performance of the classifiers used in BCI system. There are many methods used to identify, reject, and remove artifacts. However, no consensual standard metrics for performance evaluation of these methods is available. The aim of this work is to study the performance of the different preprocessing techniques in classification, using raw EEG data and power spectra. Here, the preprocessing is the bandpass filtering, filtering and artifact removal by Independent Components Analysis (ICA), and filtering and rejection of artifacts by threshold. EEG signals from six right-handed healthy volunteers were divided in three tasks: observation (elbow flexion and extension); elbow flexion movement; elbow extension movement. According to the results without feature extraction (raw EEG), filtering and artifact removal by ICA had better accuracy. In addition, with feature extraction (power spectra), the bandpass filtering is preferred because of simplicity and no loss of data, even if it showed a slightly worse performance.

Brain connectivity analysis via complex networks has been widely applied to elucidate functional aspects related to brain diseases, such as Alzheimer and Parkinson, and, more recently, to investigations concerning the functional organization of brain regions under motor imagery in brain computer interfaces (BCIs). Therefore, this work seeks to investigate the classification performance of steady-state visually evoked potential (SSVEP) brain-computer interfaces based on functional connectivity. Two different approaches were chosen for extracting functional connectivity and estimating the adjacency matrix from SSVEP-EEG signals: classical Pearson correlation and a new proposal based on Space-Time recurrence counting. These strategies were followed by graph feature evaluation (clustering coefficient, degree, betweenness and eigenvalue centralities), feature selection via Davies-Bouldin index and classification using a least squares classifier for 15 subjects in a 4-command SSVEP-BCI system. For comparison, we also employed a classical spectral feature extraction approach based on the fast Fourier transform (FFT). It was observed that it is possible to separate the classes with a mean accuracy of 0.56 for Pearson and 0.61 for the STR framework, with the clustering coefficient and the eigenvector centrality being the best attributes for these scenarios, respectively. Nonetheless, classical FFT-based feature extraction obtained the best decoding performance.

Lung cancer is the type of cancer that most makes victims around the world and often presents a late diagnosis. Computed tomography (CT) is currently the reference imaging test for the diagnosis and staging of lung tumors. Recent studies have shown relevance in the characterization of lung tumors by different sequences obtained with magnetic resonance imaging (MRI). MRI also has the advantage of not exposing the patient to ionizing radiation, as occurs in CT scans. This paper presents an investigation about the applicability of pattern recognition methods to computer-aided diagnosis of lung cancer in MRI exams. A set of 21 T1-weighted contrast-enhanced MR images associated with lung lesions (14 malignant and 7 benign) was retrospectively constructed and semi-automatically segmented. Quantitative features were obtained from tumor 2D and 3D segmentation, totaling 150 features. Unbalancing problems were solved synthetically oversampling the dataset. Tumor classification was based on five machine learning classifiers and leave-one-out cross-validation. Relevant feature selection was performed for all classifiers. Results showed significant performance on balanced dataset, presenting area under the receiver operating characteristic (ROC) curve of 0.885 during the validation, and 0.938 during the test process. The investigated approach demonstrates potential for computer-aided diagnosis of lung cancer in MRI.

Occlusion-effect and acoustic feedback are common complaints of the hearing aid user. The occlusion effect is described as an annoying quality of the user’s own voice that sounds hollow or boomy, while feedback instability results in an unpleasant loud continuous tone. Despite the availability of high performance feedback cancellers, severe to profound losses require large amplification and, as a result, certain degree of occlusion to minimize feedback. The smaller the vent, the greater is the feedback-path magnitude attenuation as well as the increase in the occlusion effect. This work presents a numerical simulation investigation about the concurrent use of the prediction-error-method feedback canceller and the feedforward-occlusion canceller in hearing aid applications. Evidences about mutual performance impact on both cancellers are pursued. The studied scenario takes into consideration three sizes of the ventilation opening of the hearing aids. Simulation results indicate that the individual performance of both cancellers is not affected when the stability is preserved. This finding can be of interest for hearing aid designers when setting up the canceller parameters.

Breast cancer is a major public health problem, being the second most common type of cancer among Brazilian women. It is known that the higher the density of the breast, the greater the difficulty of assessment, which may result in a non-detection of this disease. In addition to the difficulty of evaluating dense breast images due to the similarity between normal tissue and lesion, the mammographic image presents intrinsic problems of the acquisition process, such as noise. Thus, the goal of this work is to propose a combination of techniques for filtering and contrast enhancement in dense mammographic images, evaluating them from the calculation of the signal-to-noise ratio (SNR) and the peak signal-to-noise ratio (PSNR). For denoising, it was proposed to use the wavelet transform with an automatic threshold, while for the contrast enhancement, the contrast limited adaptive histogram equalization (CLAHE) algorithm was used. The results showed that there was an increase of 50, 5% and 16, 9% for the calculation of SNR and PSNR, respectively, when the CLAHE technique was used after the denoising. As a conclusion, the use of two combined methods, one for filtering, and one for contrast enhancement, allows increasing the signal in relation to the estimated noise of the image.

Corpus callosum is the greatest white matter structure in brain. It is located beneath the cortex and connects both of two hemispheres, making possible their communication. Corpus callosum shape and size are associated with some subject’s characteristics such as gender, handedness and age, and alterations in its structure have correlation with some diseases and medical conditions. Diffusion MRI allows a further analysis of corpus callosum structure and functionality by accessing neuronal fibers and tissues microstructure using the water diffusion model. However, the corpus callosum segmentation (required initial step to structural analysis) in diffusion MRI is challenging, since no gold-standard is available. In this work, we propose a segmentation evaluation method that relies on the corpus callosum shape by using its shape signature. We were able to evaluate three different segmentations in diffusion MRI over a 145 subjects’ dataset using manual segmentation on T1 as reference.

In 2010, the IBGE Census found that at least 9.7 million people were deaf in Brazil. From these, around twenty-two percent had severe hearing loss. The Cochlear Implant is a procedure that aims to restore one’s hearing ability through the insertion of a set of electrodes into the cochlea. Due to the complexity of this surgical procedure, the Cochlear Implant has given rise to extensive development in computational simulation. Believing it to be possible to improve results, we sought support in the literature of Yu et al. (J Laryngol Otol 129(11):1085 (2015), [6]), in the article: curvature measurement of human bilateral cochleae. This study implements Yu et al. (J Laryngol Otol 129(11):1085 (2015), [6]) cochlear curvature measurements through a free visualization and medical imaging processing platform using markers to generate a curvature map. SPL Ear Atlas was applied as a model for the curvature mapping, three regions of interest with greater curvature were observed for basal turn and apex turn, and two for middle turn. In conclusion, this study shows gradual growth of the curvature towards the apical loop, in accordance to literature. This gradual increase of the curvature emphasizes the need to characterize morphological structures in order to avoid potential damage due to the insertion of the electrode array.

Early detection of breast cancer can increase treatment efficiency. One of the earliest signs of breast cancer is the Architectural Distortion (AD), which is a subtle contraction of the breast tissue, most of the time unnoticeable. A lot of techniques have been proposed over the years to aid the detection of AD in digital mammography but only a few using a deep learning approach. One of the most successful algorithms of deep neural architecture are the Convolutional Neural Networks (CNNs). However, to assure better CNN performance, the training step requires a large volume of data. This paper presents a deep CNN architecture designed for the automatic detection of AD in digital mammography images. For the training step, we considered the data augmentation approach, to overcome the limitation of clinical dataset. CNN performance was evaluated in terms of Receiver Operating Characteristic (ROC). The measured area under the ROC curve (AUC) was $$0.87$$ 0.87 for the proposed CNN in the task of AD detection in digital mammography.

The auditory selective attention is the ability that allows the concentration on a sound stimulus of interest while ignoring other stimuli. The classification of this attention state might be done through auditory steady-state responses, being a possible application in brain-computer interfaces. A method to perform the classification of selective attention is proposed in this article, with dimensionality reduction by principal component analysis, filtering of the signals by a digital Butterworth filter and the computation of the energies of the resultant signals. The energy values are then applied to the inputs of an artificial neural network to perform the classification, obtaining a max accuracy of 64.07% with an information transfer rate of 2.7197 bits/min. So, it is shown that the classification of the effect is possible, however it is still necessary some studies to tell how much the performance of this classification can be improved.

Neurofeedback (NFB) training has been applied as a complementary or alternative treatment to several neurological conditions, such as attention deficit and hyperactivity, epilepsy, anxiety, amongst others. However, the technique has yielded controversial findings, mainly due to non-standardized and uncontrolled studies. Moreover, research indicating apparent benefits of NFB training often do not investigate how the observed behavior changes relate to subjacent neural mechanisms. In this study, our aim was to verify whether measures of functional connectivity (FC) changed with the NFB training sessions, and if such variations could be related to the subject’s subjective perception of their attention capacity. We analyzed electroencephalography (EEG) data during the resting state (RS) condition for three healthy subjects over 10 NFB sessions and assumed that there are individual, specific sets of FC links that remain for, at least, a certain fraction r of the total RS recording. By setting r  = 0.7, we computed the number of these frequent links over the NFB sessions. Overall, we found that pre and post-NFB training RS recordings tend to present distinct patterns, and that there can be specific increasing or decreasing trends for each subject. On the other hand, no correlations were found between the FC results and the subjects’ answers regarding their subjective perception of attention. Inclusion of more subjects and other experimental groups, such as control and/or false-NFB, will be performed next to provide more certain insights.

The analysis of brain signals, known as EEG, is a field of great interest, especially in rehabilitation medicine. For this area, the development of Brain Computer Interface (BCI) systems is of great value since it offers a new channel of communication between the individual and external systems through the analysis of brain signals. This work aims to analyze brain signals used in Brain Computer Interface systems using one of the most widespread techniques for such, Principal Component Analysis (PCA). An online database (Graz data set B) containing EEG signals of motor imagery activity of left and right hand was used. These signals were analyzed and their characteristics were extracted using PCA. To identify them according to the motor imagery performed, these characteristics were classified using a classification algorithm, Logistic Regression.

Peripheral neuropathies occur by injury to sensory afferent fibers. A common consequence of neuropathies is the loss of thermal sensitivity. In the clinic, there is a difficulty in quantifying and assessing damage to the thermal fibers (Aδ and C). Besides that, the identification of cerebral response to the stimulation of Aδ and C fibers is still a challenge, especially when it comes to the stimulation of non-painful cold and warm. Therefore, a signal processing technic—the Magnitude Squared Coherence (MSC)—allows to investigate the relationship between different electroencephalogram (EEG) frequency bands. The aim of this study was to investigate the cerebral response to non-painful thermal stimulation, in a steady-state stimulus, using coherence between hemispheres (inter-hemispheric coherence) and inside hemisphere (intra-hemispheric coherence). The peripheral thermal stimulation was performed in ten healthy male subjects, and the brain response was assessed by EEG. The contra-lateral intra-hemispheric coherence demonstrated a consistent statistical difference for cold stimuli for the gamma band in temporal lobe, compared to the spontaneous EEG. On the other hand, there was no difference for the warm stimuli.

In this study the efficiency of the AR, MA and ARMA models was analyzed for the prediction of a center of pressure signal in a quiet upright stance on a force platform. The analysis was performed to verify differences among the above models to predict the next sample of a signal. A small prediction error allow to predict and replace any gaps in the acquired signals, which is interesting in any area of science, especially when it comes from biological signals in its most varied aspects. The Welch method was used for spectral estimation. The prediction error presented the greatest variation when comparing the cases with raw (unfiltered) and filtered data. We observed that using these models on unfiltered signals resulted in poor prediction; however, the results indicated ARMA modeling as a good predictor using filtered center of pressure signals.

The virtual reality environment has allowed the study of the activation of the cerebral pathways and the understanding of the neurophysiological aspects from multicentric electroencephalographic monitoring and visual evoked potential related to the movement. So, the dynamic virtual scene has supported the assessment of the sensory pathways involved with the postural control and the complex integration of the visual, somatosensitive and vestibular systems, as well as the planning of the motor actions that helps in the postural adjustments. The objective of this work was to evaluate the effect of dynamic visual stimulation with the use of stereoscopy to elicit Motion-Related Visual Evoked Potential (M-VEP) through 3D virtual scene. In this study, twenty-five healthy volunteers have participated, 8 women and 17 men, aged 28.24 ± 7.06 years. The M-VEP has shown a significant difference (p < 0.10) in the time range related to the P3 component in all derivations except Fp1, F8, Pz, O1, Oz and O2. A pattern of greater latency variability was identified in the DSF stimulation condition, and a greater variability of the amplitude related to DSB. In addition, it was observed that the M-VEP related to the approximation scenario presented higher latency in the occipital and parietal region, pointing to a possible delayed perception of the movement of this scenario. Nevertheless, more studies are necessary to conclude this possible delay in the integration of somatosensory information as strategy of postural control.

Human-machine interaction has growing interest in studies about recognition of human emotions since these are relevant to the establishment of social relationships. Some markers of human emotions are physiological signals, such as heartbeat, brain signals, sweating and skin temperature. Many of them are captured through sensors with contact with the body, however, contact-free (unobtrusive) sensors are currently studied targeting those people who have sensitivity to touch, such as the case of people with Autism Spectrum Disorder (ASD). InfraRed Thermal Imaging (IRTI) is an unobtrusive technique for recording thermal variations on the skin. The goal of this work is to investigate the thermal variation from two emotions (happiness and sadness) on the face of five typically developing children through IRTI. The facial regions of interest (ROI) are forehead, cheeks, tip of nose and periorbital. This investigation is based on the brightness variation between the exposition to two emotional stimuli (evoked by videos) and the absence of stimuli (baseline). Values from Student’s t-test indicated significant differences between both emotional states (positive/happiness, and negative/sadness) and the baseline.

Cardiac MRI has experienced a crescent relevance in clinical investigations. The segmentation of myocardial walls is a prerequisite for assessment of cardiac viability. Manual or semi-automatic segmentation of all the images of a subject is tedious, as well as consuming much time from cardiologists. In this study, we selected 23 slices of simulated cardiac MR by MRXCAT and 30 real slices of CINE-MR from 15 patients with Chagas Disease. The proposed pipeline of the fully automatic segmentation consists of three steps: 1. Preprocessing; 2. Automatic Seeds Definition; and 3. Segmentation by Geodesic Active Contour. An experienced cardiologist provided the gold standard annotations of apical, mid-ventricular and basal LV myocardium. We use the following three metrics to validate the proposed pipeline with different signal to noise ratio: Dice similarity (DS), Precision (Pr) and Volumetric Similarity (VS). DS show good agreement between manual segmentation and the automatic segmentation in simulated images with SNR 200, 25, 15 and 5, i.e., 0.98, 0.93, 0.9 and 0.72, respectively. We found moderate agreements between manual segmentation and Snake segmentation in simulated images with SNR 200, 25, 15 and 5, i.e., 0.38, 0.42, 0.34 and 0.39, respectively. The DS, VS, and Pr obtained suggest substantial agreements between the manual and our proposed method segmentation in images of Chagas’s Disease, i.e., 0.8 [0.69–0.87], 0.89 [0.72–0.99] and 0.9 [0.76–0.98] (mean [min–max]), respectively. Our findings suggest that one can use the proposed method in the automatic myocardium segmentation with reliability similar to manual tracing, although completely free of human interaction.

Digital signal processing has several applications in biomedical engineering and it is possible to observe the great technological progress resulting in digital devices. Electromyographic signals are an important source of information regarding biological parameters but generate a large amount of data when digitized. This work presents the evaluation of HEVC (High Efficiency Video Coding) encoder operating in intra mode applied to the compression of surface electromyographic signals (sEMG) of three different dynamic exercise protocols: (i) Increasing power and constant speed; (ii) Constant power and increasing velocity; (iii) Constant power and constant speed. Objective evaluation metrics are applied in the analysis of the results: the percentage root mean square difference (PRD%) and the compression factor (CF%). Different behaviors of the encoder were observed when different protocols were applied and with different rectangular temporal window lengths. Competitive results were obtained in comparison to the reference literature with the signals of the third protocol (iii) under a window of 8192 samples, when CF of 85, 90 and 95% was applied.

In the present work, the Event-Related Synchronization and Desynchronization (ERS/ERD) index, expressed as a function of the Spectral F-Test (SFT), was applied to evaluate motor response uncertainty. The aim of this study was to investigate the EEG synchronization prior to the motor response in order to intercept a ball with unknown trajectory. A protocol of soccer-penalty simulation in 3D virtual reality environment was proposed, composed by a ball interception procedure that simulates the defense from the goalkeeper view, divided into three types of stimuli: left (LB), right (RB) and centered ball. The EEG signals of 27 right-handed adults were assessed using ERS/ERD index, and the McNemar test was then applied in order to compare the proportions of synchronization occurrence for the same channels and frequency bands between LB-RB with 0.05 of significance level. The results indicate a consistent occurrence of ERS in gamma band, but not for alpha and beta bands. Also, the synchronization occurrence seems to differ depending on the direction of the ball trajectory, particularly in gamma band. These findings support the need of further studies to provide important parameters to analyze neural activity during motor preparation.

Breast cancer is the one that most affects women worldwide. Mammographic examinations are considered the best way of detecting non-palpable breast lesions, although there are several factors that make difficult the diagnostic accuracy in breast cancer screening. Computer-aided diagnosis (CADx) systems have been developed aiming to help breast cancer diagnosis. They are based on microcalcifications (MCs) characteristics and can be treated as a three-step system: (i) MCs segmentation; (ii) extraction and selection of features from the segmented MCs; (iii) lesions classification. In this work, a set of 25 morphological features were extracted from lesions containing MCs presented on 190 mammographic images. The MCs of these images were segmented and binarized in a previous work, using Mathematical Morphology techniques. After extracting the morphological features, a selection method based on mutual information ranked the features, and a linear discriminant analysis used a forward procedure to search, among the ranked features, the best subset to classify the lesions. The best classification performance was achieved with 4 morphological features: Fourier Factor, Zero crossing, Long-axis to short axis ratio and Mean value of the Normalized Radial Length.

According to World Health Organization, light microscopy is the diagnostic standard of malaria. This diagnosis requires examination of both thin and thick films from the same patient. However, in most large health clinics and hospitals, the quality of microscopy-based malaria diagnosis is frequently inadequate. Automatic microscopy diagnosis allows an increase in the number of fields of view to be analyzed, providing more accurate diagnosis, while reducing the time required for that purpose. Automatic focusing of the microscope is an essential component of automated microscopy; it is the first step of automated malaria diagnosis. In this work, we implemented the “classical image-analysis-based auto-focus techniques” approach using nine-focus function in order to identify the best focus function for thick blood smear images. Because some previous works have shown that the accuracy focus functions sometimes depends on content of the processed images, and the specimen can determine which metrics should be more adequate, we proceeded two experiments. In experiment #1, we evaluated the focus functions in an image-stacks dataset (338 stacks and 5 images/stack). Then, we did experiment #2, this time, testing with patch images (fragments) containing Plasmodium vivax in its various life cycle phases (ring or immature trophozoite, ameboid trophozoite, schizont and gametocyte). The parasite dataset used contained 1713 patches. Brenner gradient focus function was the best in both experiments.

This work presented a method to automatic classify six hand and wrist gestures (wrist flexion, wrist extension, wrist flexion to left, wrist extension to right, supination, and pronation) using multichannel sEMG signal features from the forearm and machine learning techniques. Data were collected using a wearable armband and the signal processed in LabVIEWTM platform. Six classifiers were evaluated: Multi-Layer Perceptron (MLP, an Artificial Neural Network), K-nearest neighbor (k-NN), Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Decision Tree (DT), and Naïve Bayes (NB). The method demonstrated to be suitable, achieving high overall accuracy (over 90%) and up to 99% on single movements using MLP with 31 hidden layers. Other methods, such as LDA, QDA, k-NN and DT, have shown accuracy around 80% and therefore must not be reject due to its low computational complexity.

It is increasingly essential to monitor clinical signs and physical activities of elderly, looking for early warning signs or to recognize abnormal situations, such as a fall. In recent years, the usage of wearable sensors has increased significantly. Data from wearable devices can be used to recognize human movement patterns while performing various activities. Accelerometers have been widely used in human activity recognition systems, however, instead of traditional techniques used for feature extraction, the scientific community is currently developing classifiers based on deep learning techniques, seeking better performance and lower computational cost. Convolutional neural networks (CNN) are the main deep learning technique used in this context. These networks adjust filter coefficients that are applied to small regions of the data, extracting local patterns and their variations. This paper presents a human activity recognition system based on convolutional neural networks to classify six activities—walking, running, walking upstairs, walking downstairs, standing and sitting—from accelerometer data. Results demonstrate the ability of the proposed CNN-based model to obtain a state-of-art performance, with accuracy of 94.89% and precision of 95.78% for the best configuration.

This paper proposes an image processing pipeline to improve the detection of vertical root fractures (VRF) in digital periapical radiographs. The pipeline is composed by three steps: geometric adjustment, Gamma correction and adaptive contrast enhancement. The proposed method was compared to the conventional pre-processing pipeline normally applied to digital radiographs. Four experienced dentists (two radiologists and two endodontists) classified a set of digital periapical images according to the presence or absence of fracture, when images were pre-processed using both methods. Inter-examiner reproducibility was verified by the weighted Kappa test. Sensitivity, specificity and accuracy were calculated along the area under the receiver operating characteristic (ROC) curve for both methods. Results indicated that the proposed method increased the specificity of radiologists by 13% and the endodontists by 4% when the proposed pipeline was used. The proposed method yielded better results on the detection of VRF in digital periapical radiography images when compared to the traditional method.

The aim of this study were: to compare image quality obtained with a natural juice (açai) and a manufactured contrast employed as negative oral contrast agents in Magnetic Resonance Cholangiopancreatography (MRCP) exams. After in vitro experiments with several juice samples of pineapple and açai fruits, açai juice was chosen as natural contrast agent. 64 patients were submitted to MRCP exams in a clinic of Curitiba city (Brazil), on 2 consecutive days. On the 1st day a manufactured contrast was offered to patients and, on the 2nd a natural juice. 2 radiologists (R1 and R2) evaluated the images, classifying them by a score and checking if a picture could replace the other, for the issuance of a medical report. Evaluating images on the 1st day, R1 furnished an average score of 3.52 and R2 of 3.27. On the 2nd day, R1 provided 3.44 and R2 3.38. Also, R2 provided better scores for natural juice. The evaluators considered that image quality with contrast was adequate for 62 (96.9%) patients. With juice, R1 and R2 considered adequate for 62 (96.9%) and 60 (93.8%) patients, respectively. Considering both radiologists, image quality was better than 93.8% and, equal to 96.9% for juice and contrast, respectively. Thus, açai juice can be employed as an alternative to contrast agents in MCRP exams since a standardized protocol is implemented.

In this paper it is proposed an alternative method for detecting electromyographic (EMG) activity using inertial sensors, together with a comparison between the performance of the proposed method with two computer-based methods available in the literature. Visual inspection was adopted as the golden standard for detecting EMG signals. The comparison was made by calculating the difference of time between the detection of a trained observer and the time annotated by 3 different computer based-methods. The EMG signal was captured from 12 individuals while performing wrist extension from the extensor and flexor muscles of the wrist. The results showed that the proposed algorithm is superior to detect EMG activity when compared to the traditional ones. In conclusion, the use of inertial sensors together with electromyography reduces errors in the estimate of the beginning of the activity, when compared to the case in which the only available information is electromyography.

In this work we present a preliminary study regarding the use of MODWT decomposition and time domain parameters for the task of hand gesture classification using sEMG signals. A total of 28 features were extracted for each subject and a first test has shown an improvement of up to 5.8% in relation to previously work. As expected, a simple linear classifier (LDA) obtained the best results. As a second test, we attempted to evaluate the classifiers with respect to the prediction of each hand gesture and mixing the individual data of the subjects. Unlike the first test, with a more complex approach, the Autoencoder technique performed an average accuracy of 77.96% ± 1.24 against only 65.36% ± 1.09 achieved by the LDA classifier. Each classifier fails mainly to separate the gestures belonging to the same grasp group: precision grasp (Tip, Palmar and Lateral) and power grasp (Cylindrical, Hook and Spherical), because of their similarities from a muscular point of view. This result led us to perform a final test considering only precision/power classification, reaching an average accuracy of 95.60% with the Autoencoder Neural Network. In general, the results illustrate that the method presented in this paper can be applied to real applications; but a more refined approach, with more subjects and gestures, should be done.

This work presents a non-invasive method to identify the ingestive behavior in ruminants using Surface Electromyography (sEMG) of the masseter muscle. It was evaluate whether the rumination and intake process could be distinguished using sEMG signal features and machine learning techniques. To collect chewing sEMG signal, superficial Ag/AgCl electrodes were placed on two ruminant animals masseter muscle and the data was sampled during eating with an analog-to-digital converter. Three segmentation techniques was explored and applied to automatically subdivide the chewing movement signal. Five classifiers (k-nn, LDA, SVM, NB and DT) were evaluated using four features (RMS, SSC, ZC and WL) extracted from the signal. We found and accuracy over 93% using a fixed length segmentation method and k-nn for classification with $$ k > 7 $$ k > 7 . Future works may explores the implementation of time series analysis to improve the classifier performance.

Among all cancers, lung cancer (LC) is one of the most common tumors, presenting an increase of 2% per year on its worldwide incidence. In Brazil, INCA (Instituto Nacional de Câncer Ministério da Saúde (2018). http://www2.inca.gov.br/wps/wcm/connect/tiposdecancer/site/home/pulmao , [1]) estimates the occurrence of 31,270 new LC cases, being 18,740 among men and 12,530 among women, in 2018. In this work, we propose a method for seeds initialization for lung segmentation. The method goal is to spread seeds through the lung in its base, hilo and apex. The purpose of this technique is to increase the segmentation method speed and decrease the amount of needed iterations. The results were obtained by using 40, 60, 80 or 100 beams with lengths varying from 40, 60, 80 to 100 voxels. The beam length was also varied with 40, 60, 80 and 100 voxels. The difference of the proposed work to the methods in the literature is the different subregions. The proposed method will create different seeds over the three regions of apex, hilo and base for each lung.

Compressed sensing (CS) techniques are an active area of research that can potentially reduce magnetic resonance (MR) imaging acquisition times. The recent success of deep learning techniques have inspired the CS community to investigate the use of neural networks and other machine learning techniques for MR image reconstruction. Although convolutional neural networks (CNNs) are attractive techniques because they have few parameters and are not image size dependent; but when compared to fully connected neural networks, they have a limited receptive field. In this paper, we will investigate the use of fully connected neural networks for CS. A similar technique has previously been used to reconstruct images from truncated MR data. Nonetheless, this demonstration was before recent advances that have shown that in order to do CS successfully, it is necessary to employ random, incoherent data undersampling. Here, we show that a one-layer fully connected neural network can be equivalent to the inverse discrete Fourier transform (iDFT) typical used to reconstruct fully sampled data. This suggests that the Fourier weighting coefficients can be learned. We compare our approach with CNNs and validate the results on 1D signals. These results can be generalized to 2D signals.

This study implements the novel high-density surface electromyography (EMG) to investigate the distribution of pectoralis major (PM) muscle activity during the inclined bench press exercise. Six healthy male individuals participated in this study. Subjects performed one set of eight repetitions of the inclined (45°) bench press exercise. They used 70% of their one-repetition maximum load. EMGs activity was recorded with an array of sixteen electrodes perpendicularly placed above PM muscle fibers between innervation zone and sternocostal tendon region. For each contraction, the root mean square value (RMS) was computed. Additionally, active channels were defined as EMGs with RMS amplitude greater than 70% of the maximum amplitude for each contraction, respectively. Considering the active channels, the barycenter coordinate was calculated indicating the mean position of the RMS distribution along cranio-caudal axis of PM muscle. For all the volunteers, we identified the barycenter coordinate located near to the clavicular portion, indicating that there is a localized activation on PM during 8 repetitions of the inclined bench press.

This paper presents computer simulations of Ultrasound Tissue Harmonic Imaging (THI) and Pulse Inversion Harmonic Imaging (PIHI) models. The main goal is to augment the detection of targets that would not be possible using fundamental frequency imaging (Conventional B-mode Imaging, CBI). After computer modeling and validation, we expect to implement those techniques in a technological platform for ultrasound research. THI and PIHI techniques are described and simulations were carried out using 32 element matrix transducers with fundamental center frequencies 1.5 and 3.5 MHz. We have used a phantom proposed by Treeby and Cox [1] using the K-Wave toolbox implemented in Matlab®. Our results showed that, when using harmonic imaging techniques, it was possible to detect structures that were not or were poorly visible in conventional B-mode imaging.

The process of obtaining cardiological parameters in echocardiography images demands profound experience of the professional who analyses the images. The segmentation of the heart facilitates the obtaining of parameters in those images and benefits the diagnosis process. The present work objective was to develop a method for the segmentation of the left ventricle (LV) in echocardiography images of parasternal long-axis view from distinct databases that exhibits diversified quality. The database as a whole used in this paper consists of 67 two-dimensional gray-level echocardiograms recordings. Kohonen´s Self-Organizing Map and the Polynomial Interpolation were used to find the endocardial and epicardial walls of LV in those images. The Dice coefficient was used to compare the segmented region to the region defined by the contours drawn by a cardiologist. The mean Dice coefficient was 0.93 ± 0.02. The result validation by the Dice coefficient suggests that the system developed for the segmentation of the LV may be usable in distinct echocardiography image databases.

This paper proposes the minimization of the distortion measure of signals reconstructed by wavelets using genetic algorithm. The minimization happens between the original study object and the reconstructed one—Percent Root-Mean-Square. This distortion measure is analyzed in wavelet creation that are responsible of signals reconstruction with the best approximation from its originals, and these signals, used as study object, are electrocardiograms signals. This proposal solves the problem main commonly found in signals compressing area which is exactly the reconstruction of signals without loss of informations. The proposed method provided satisfactory results compared to those found in the literature.

In this paper, it is proposed the modeling of shape attributes of the BI-RADS lexicon for distinguishing between oval, round, lobulate, and irregular lesions. Two multi-class decomposition schemes are evaluated: OVO (one-versus-one) and OVA (one-versus-all), where the linear discriminant analysis is the base classifier. Moreover, both approaches are trained with 17 morphological features invariant to geometric transformations. The experiments considers an image dataset with 1434 mammographies: 820 benign and 614 malignant lesions. Also, the accuracy index (ACC) is used to evaluate the classification performance, where the OVO scheme performed better than the OVA scheme, with $${\text{ACC}} = 0.897$$ ACC = 0.897 and $${\text{A}}CC = 0.818$$ A C C = 0.818 , respectively. Besides, the t-Student test $$(\alpha = 0.05)$$ ( α = 0.05 ) reveals that both approaches are statistically significantly different $$(p < 0.001)$$ ( p < 0.001 ) . The results suggest that modeling the shape attributes is feasible, where the OVO scheme is recommended for this task. Moreover, the proposed approach can be extended to model other terms of the BI-RADS lexicon such as margin and density.

The ability to communicate without using speech or hand gestures poses a great improvement in the quality of life of patients that suffer from movement impairment. Human-machine interaction tools are being studied and developed in order to optimize the usage of biological signals not affected by the individual’s disease. Among different approaches electrooculography signals are an alternative for those who can still move their eyes. This work proposes the use of Genetic Programming to interpret bio signals in the control of a mouse cursor. A digital system was designed to record and filter the EOG signal. Thereafter a Genetic Programming algorithm was used to find the best description for the cursor movement. We show that the algorithm was able to find an equation that describes the moment with 92.5 and 93.0% hit rate for each subject respectively. These preliminary results are compatible with the literature and show that Genetic Programming can be used to find a description of a cursor movement in a simple EOG system with no need of prior knowledge about the movement neither threshold definition.

Stroke is an injury that abruptly affects brain tissues. This disease is caused by a change in the blood supply to a particular region of the brain, and it results in the loss or reduction of its related functions. Cerebral vascular accidents affect 16 million people worldwide every year, and 6 million of these people die. However, another important problem related to strokes, besides mortality, is that many survivors have chronic consequences that are complex and heterogeneous. In this paper, a new approach to identify and classify stroke from a structural co-occurrence matrix (SCM) in skull CT images. To state the efficiency of the technique considered, a comparison with other important and well-known state-of-art feature extractors was performed. In addition, SCM was evaluated with two high-pass filters (Laplacian and Sobel) and two low-pass filters (Median and Gaussian). Regarding classifiers, Bayesian classifier, Optimum-Path Forest (OPF) and Support Vector Machines were used. The proposed approach using its optimal configuration and the OPF classifier with Euclidean distance had the highest average accuracy (99.40%), and the extraction time low, similar to other ones widely used and well known.

Digital breast tomosynthesis (DBT) is an emerging imaging modality used for breast cancer screening. This method minimizes the limitation of tissue superimposition from conventional digital mammography. The reconstruction of the 3D volume from radiographic projections is an important step and it is still a challenging task. Clinical units from various manufacturers differ in acquisition geometry, and thus different reconstruction parameters must be set for each of them. We have developed an open-source MATLAB toolbox for DBT reconstruction. The proposed toolbox is intended for academic usage. The virtual Shepp-Logan phantom and a physical BR3D phantom were used to validate the proposed implementation of the reconstruction methods and the acquisition geometry of the clinical unit. Visual assessment of the reconstructed slices obtained with the proposed toolbox indicates that the reconstruction was performed successfully. The results were evaluated using a structural similarity metric. Visual comparison against the slices obtained with the built-in commercial reconstruction software shows the potential of the proposed reconstruction pipeline, which can be easily modified to incorporate sophisticated algorithms and accelerated using parallel computation techniques.

Hearing aids are of paramount importance in the life of millions of hearing impaired people around the world. Profound to severe hearing impairment requires large amplification of the input sounds and may lead to acoustic feedback if the ear mould does not adequately closes the ear canal. In such a situation, hearing aid users usually complain that their own voices sound hollow or boomy when they are talking or chewing, this characterizes the well-known occlusion-effect. To overcome this problem active cancellation techniques can be applied. This work presents a statistical analysis of the deficient length feedforward occlusion-effect canceller for hearing aids. Deterministic recursive equations to the mean coefficient and mean square error behaviors were derived for predicting the canceller performance in both transient and steady-state periods. Comparisons between Monte Carlo simulations and theoretical results show very good agreement. The derived models can be useful for hearing aid designers to reduce the computational cost of the canceller with a controlled performance loss.

The injection of contrast medium is fundamental for diagnosing and differentiating liver cancers in Computed Tomography (CT). Computer-aided diagnosis systems have been developed to support radiologists in detecting liver lesions. However, the injection of contrast medium may change features and thus affect the automatic segmentation of the liver. In this paper, the performance of an automatic segmentation algorithm using a posteriori information of the liver was compared between the arterial and portal contrast-enhanced CT phases. By performing the liver segmentation with a region growing algorithm based on one-class support vector machines, 28 CT scans in both arterial and portal phases were evaluated. In general, the results show that there were no differences between segmentation performances for both phases. Major segmentation errors were usually related to intrinsic characteristics of the liver. Therefore, based on the results presented in this paper, it was possible to conclude that different contrast-enhanced CT phases do not affect liver segmentation significantly.

Research indicates out that early diagnosis of the tumor may increase the chance of disease treatment and currently, the most effective method for early detection of illness is mammography. For this reason, using techniques that result in better quality images, that is, with as little noise as possible can aid in the visualization and detection of lesions and improve the accuracy of the diagnosis. For this work were used three filters being them: Wiener, Lee and Frost and to measure its efficiency were used the following parameters: Peak Signal to Noise-Ratio (PSNR), Signal to Noise-Ratio (SNR) and Structural SIMilarity (SSIM). It was verified that the Wiener filter presented better performance when compared to the others.

Machine Learning (ML) has drawn a lot of attention these days due to its capability to automate processes that where very complicated and/or only performed by humans before. It is done by not having the need to write hard coded rules to solve problems, letting the machine find the rules by itself, and for being able to universally approximate functions with certain algorithms. On the other side, motion capture systems are quite expensive, making it more difficult to health professionals and physicians to have a more precise way to analyze the gait of patients and the diseases related to it. Having that said, this paper aimed to show that is possible to predict (generate) the angles of the right knee of patients directly from a common video of their walks, reducing costs and opening the opportunity for a more complete and affordable system to realize motion capture. Thus, this study is an initial experiment and the first step towards a possible future more complete product. As an initial study, the data of three patients were acquired and the resulting model, a Multilayer Perceptron Artificial Neural Network (MLP), acquainted a score of 92.2% when predicting the angles of the right knee for unknown data.

There are many studies in the machine learning field for classifying movements using electromyography (EMG) signals and some of them achieve high classification rates. The cost for good performance although, is the long time necessary to train the classifiers. This work proposes a multi-class Support Vector Machine (SVM) running in hardware. It is part of a bigger project which aims to train and classify movements maintaining good classification rates in reduced time. For testing the hardware solution, 12 channels of RMS extracted from surface EMG (sEMG) data available at Ninapro Database were used to classify 17 movements. Results reveal a 59.2% mean classification rate for the proposed system implemented in FPGA when running few milliseconds against 58.2% obtained by Matlab in the same scenario.

Magnetic resonance (MR) imaging has been largely used as a diagnostic imaging modality. Especially in brain image analyses, MR allows the detection of subtle abnormalities, aiding in the patient diagnosis and follow-up. Driven by improved acquisition techniques, the amount of MR data to be processed and analyzed have risen exponentially. So too has its variability, particularly in multi-center studies which potentially include images acquired at different centers or using different acquisition sequences and parameters. Methods that aim to automatically evaluate quality in these large datasets are required to assure data correctness and completeness, and also to identify image artifacts and potentially patient abnormalities. We propose a quality control framework to detect prospective outliers sets in brain MR imaging datasets that is based on imaging features, such as image contrast, intensity and texture. The framework allows the selection of individual or the combination of features, over the entire database or over a subset data based on metadata information, such as gender or an age range. Experiments have shown that the proposed framework is capable of detecting imaging datasets with artefacts, such as motion, and also brain abnormalities, such as lesions.

Spectral heart rate variability analysis is commonly used as a non-invasive measure of cardiac autonomic regulation. These indices are traditionally based on power spectral density estimation of short data segments (e.g. 5 min), since stationarity of the heart rate variability signal is required for traditional spectral estimation. To study autonomic regulation of cardiac function as a result of time-varying interventions, a time-frequency analysis, which generates a time-varying spectrum and, thus, time-varying spectral indices, is usually a more suitable approach. To investigate the influence of the application of stationary or time-varying methods on heart rate variability before and after an autonomic challenge, this paper compares static and time-varying indices before and after slow and fast passive changes in posture. The results show a significant decrease in the high frequency index and an increase in the low-to-high frequency ratio after slow tilt only, compared to baseline, using either approach, indicating a shift to sympathetic dominance after tilt, as would be expected. The results also show a high correlation between each index (static vs. dynamic), suggesting that the autonomic adaptations to slow passive tilt in healthy subjects are fast enough to be measured by either approach.

One of the most important steps to determine the appropriate treatment for patients with breast cancer is the assessment of the hormone receptors status, which is done, most of the times, through the methodology of immunohistochemistry (IHQ). This technique allows the identification of cells with positive hormone receptor; therefore, they are effective to the hormone treatment, which when associated to other therapies, increases life expectancy to the patients. The qualitative and quantitative assessment of these receptors is done based on an analogic form; consequently, the results may vary and may be also subjective. With the technology’s advance, it is possible to automate, besides preparing exams, interpreting them, being beneficial to various patients and achieving more straightforward results. To do so, this research paper proposes the development of an imaging-processing tool for digital histological slides, with the quantification of the nuclei of the neoplastic cells in the histological sections. With the proposed method, we achieved reasonable results that were additionally validated by a pathologist, proving the efficiency of the method (about 5% of difference). The main achievement of such method is to be low-cost if compared with newly expensive technological approaches.

The aim of the present study is to contribute to medical diagnostic by applying a selection method of relevant Radiomic features from PET/CT images to help identifying adenocarcinoma in Non-Small Cell Lung Cancer (NSCLC). This work is based on radiomics techniques that allows qualitative and quantitative high performance analysis, from the calculation of radiological and molecular characteristics from PET/CT images. The Chang-Gung Image Texture Analysis (CGITA) was used to extract texture features based on morphological characteristics (shape, volume, surface area, density and mass), statistics (attenuation histogram) and regional (intra-tumor neighborhood analysis) in the region of interest (ROI) using the maximum, mean and metabolic volume standardized uptake values (SUV) considering tumor volume semi-automatically segmented ally. The CGITA returned 72 features of 24 selected images from The Cancer Imaging Archive TCIA database. They were analyzed using principal component analysis (PCA) to reduce data dimension in order to optimize computational effort and make adenocarcinoma identification more efficient. The results showed that three features based on co-occurrence matrix (contrast, entropy and dissimilarity) were responsible for more than 95% of the full variance of the data.

A Sign Language is a structured set of corporal gestures used as a communication system, which uses movements of the arm, hand, forearm, facial expressions, and lips movements to ease the communication among deaf and/or hearing people. In Brazil, the official Sign Language is called Libras. This work presents the recognition of static alphabet of Libras (20 letters) using the armband MyoTM and a Multi-Layer Perceptron. MyoTM captures Electromyography signals from forearm and these signals are used to classification. The data were acquired from one male subject, 42 times for each gesture. The signals were segmented in periods of 750 ms using onset technique and 10 features were extract from these segments. The built MLP has one hidden layer, one input layer, and one output layer, trained by the backpropagation algorithm. The number of neurons in hidden layer was tested from 10 to 300 and the best approximation for MLP was 230 neurons. The classification has an accuracy of 91.3 ± 0.5% in training and 81.6 ± 0.9 in the test. Finally, the gestures presented accuracies above 80%, except the gestures ‘L’, ‘R’, and ‘W’.

This paper proposes evaluating three convolutional neural network architectures for recognizing hand configurations of the Brazilian Sign Language (Libras). To improve the generalization of neural networks, two techniques were employed: dropout and L2 regularization. A proprietary database consisting of 12.200 depth images, captured with the Kinect® sensor was used. Two hundred images were captured for each one of 61 Hand Configurations (HC) of Libras. The training and testing subsets were compounded using an interleave technique. An accuracy of 98% was achieved. This value is better than previous results obtained, with the same dataset, using the k-Nearest Neighbor (kNN) and Novelty classifiers, 95.41% and 96.31%, respectively.

The Electrocardiography (ECG) signal carries the information about the electrical activity of the heart, playing very important role in the medical diagnosis of cardiovascular diseases. As the cardiac cells contract, action potentials arise and cause voltage variations, which are measured as ECG signals by surface electrodes placed on the body surface. Unfortunately, large amplitude signals of similar frequency arise and often reach to the skin surface and mix with the ECG signals. So, the ECG may be corrupted by many types of noise such as power line interference (PLI), which can restrict the accuracy of ECG’s heart rate detection algorithms. Several techniques have been proposed over the years to reduce PLI present in the ECG signals like hardware techniques, Notch Filters, Wiener Filters and Adaptive Filters. This paper presents known algorithms of two different approaches to adaptive filters: the NLMS algorithm, based on the classic LMS algorithm and the RLS algorithm. We compared the performance of these algorithms in an adaptive FIR filter structure called adaptive noise cancellation to remove the PLI in ECG signals. The NLMS algorithm needed more time to converge to the lowest squared error, but when it does, this error is lower compared to RLS algorithm. The RLS algorithm, in its turn, has presented a higher computational processing time and a reduced improvement in signal to noise ratio. However, its convergence speed was faster than the NLMS algorithm and it was more efficient in removing superior harmonics from PLI in the noisy ECG signal.

The article presents a proposal to detect the heart beat and an estimation of the instantaneous heart rate based on a video, using the variation of skin tone as a function of the blood flow, which are imperceptible to the human eye. This method of data acquisition does not require a sensor to be in contact with the user’s skin, so it is a non-invasive method, easy to acquire and can be performed from a certain distance. Using computer vision, it was possible to track the face over time using the KTL classifier to increase tracking accuracy and decrease the processing time. With the detection performed over time it was possible to visualize the variation of skin tone as a function of blood flow through a temporal filter and an equalization of the histogram. The final result was obtained by evaluating the histogram resulting from each processed frame of the video.

The genetic algorithm was used to obtain sparse arrays with 8, 16, 32 and 64 transducers in a possible aperture size of 128 elements. The optimization was made by minimizing a fitness function that takes into account the mainlobe width and sidelobe levels of the array radiation pattern. The sparse arrays were used to image 11 twisted wires in a medical phantom. The sparse arrays were able to generate images with resolution comparable to a 128 elements array and reduced artifacts in comparison to arrays with the same number of elements.

The Electroencephalogram (EEG) has been the most preferred way of recording the brain activity due to its noninvasiveness and affordability benefits. Information estimated from EEG has been employed broadly, e.g., for diagnosis or as input signal to Brain Computer Interfaces (BCI). Nevertheless, the EEG is prone to artifacts including non-brain physiological activities, such as eye blinking and the contraction of the muscles of the scalp. Some applications such as BCI systems may occasionally be associated with frequent contractions of muscles of the head corrupting the EEG-based control signal. This requires the application of a number of filtering techniques. However, standard gold techniques for signal filtering still contain limitations, such as the incapacity of eliminating noise in all EEG channels. For this reason, besides studying and applying filtering techniques, it is necessary to understand the contamination from electromyogram (EMG) along the scalp. Several studies concluded that EMG artifact contaminates the EEG at frequencies beginning at 15 Hz on the topographic distribution of the energy that encompasses practically the entire scalp. Thus, the present work aims to quantitatively estimate EMG noise in 16 bipolar channels of EEG distributed along the scalp according to the 10–20 system. This estimation was based on an experimental protocol considering the simultaneous acquisition of EEG and EMG of five facial muscles sampled at 5 kHz. The protocol consisted in activating facial muscles while listening to 15 beep sounds. The evaluated muscles were occipitofrontalis (venter frontalis), masseter, temporalis, zygomaticus major, orbicularis oculi and orbicularis oris. The mean power of the EEG contaminated by EMG of facial muscles contractions was compared between the periods of muscle contraction and non-contraction. The results show that occipitofrontalis and masseter muscular contamination is present over the scalp with increase from 63.5 μV to 816 μV and from 118.3 μV to 5,617.9 μV, respectively.

This study aimed at applying spectral methods and CWT in the periodicity characterization in simulated/backscattered A-mode ultrasound signals. The experimental setup used a transducer (7.5 MHz) on a pulse-echo configuration. A copper-rods phantom modeled a periodic scatterers pattern (diameter = 0.5 mm, spacing = 2.5 mm). The transducer was translated parallel to the phantom to collect signals. A-mode 7.5-MHz signals were also simulated using a numerical model with contributions from regular (MSS = 1 mm) and diffuse scatterers, with several levels of noises and jitters (variability on the expected scatterer positions). Experimental and simulated signals were processed with the spectral autocorrelation (SAC), singular spectrum analysis (SSA) and the quadratic transformation (Simon’s method) for MSS estimation, as well as the continuous wavelet transform with two mother-wavelets. MSS distributions are not similar in many noise-jitter situations, or when comparing the performance between two methods (p < 0.05). SAC was able to better characterize scatterer spacing. SIMON and CWT presented modes near 2.5 mm and 3.5 mm (the MSS and the diagonal distance of two rods). SSA may detect rods thicknesses (0.5 mm). CWT would fit for locating scatterer position.

In recent years, several studies about the generation of ultrasound tomographic images (USCT) have been proposed. The initialization of the medium to be reconstructed has a great impact on the final result of some of those algorithms. Taking advantage of the reflection and transmission phenomenon, interesting results have been obtained which extract information from the propagation medium. However objective studies about the effects of initialization in USCT reconstruction are lacking. To contribute to this topic, our work presents, on simulated medium, a comparison of the performance of a USCT algorithm with two types of initialization, one with traditional initialization (homogeneous medium) and other with information coming from other techniques. In this study, the following were used: Distorted Born Iterative Method (DBIM) as the USCT-algorithm; a Matlab Toolbox (k-wave) for the simulation; synthetic transmission aperture as edge information extractor; Reflection mask to limit the reconstruction area; Simultaneous Algebraic Reconstruction Technique for transmission tomography algorithm. Two sets of data were generated: the first with speed of sound between [1400–1680] m/s and the second with speed of sound between [1250–1830] m/s simulating a higher contrast medium; Normalized-Root-Mean-Square-Error (NRMSE) and the Structural similarity (SSIM or Q) were utilized for the evaluation. The investigation, based on simulations, suggests that the use of strategies that delimit or decrease the number of variables to be found (mask) and that applying information from reflection and transmission phenomenon to USCT-algorithms can improve its performance, offering better reconstructions and consequently more accurate information of the medium.

The human brain can be compared to a “bundle of wires” composed by neurons that interconnect distinct gray matter regions. These “bundle of wires” are the principal composition of the white matter and has the fundamental function of conducting the synaptic signals to the gray matter. Functional magnetic resonance imaging (fMRI) corresponds to a neuroimage modality optimized to quantify neural activity that occurs in the gray matter, whereas the Diffusion Tensor Imaging (DTI) is another neuroimage modality optimized to quantify distinct white matter properties. Despite the resulting signal of these image modalities come from different tissues, they are signals that contain complementary information. In neurodegenerative diseases such as Multiple Sclerosis (MS), the myelin sheath of neurons are damaged, and can provoke a series of dysfunctions such as motor disability, problems with speech, visual problems, fatigue, among other complications. Knowing the existence of this intimate functional and structural correlation among white and gray matter, this study seeks to unify both, functional connectivity measures of fMRI and structural connectivity measures of DTI, using the statistical tool joint independent component analysis (J-ICA). Results show that the coupling of these different imaging modalities is modulated by scores derived from neuropsychological tests used to evaluate patient’s cognitive impairment by MS.

The objective response detection (ORD) techniques such as Global Spectral F-Test (GSFT) are mathematical methods used to detect potentials evoked to rhythmic stimulation that are embedded in the ongoing electroencephalogram (EEG). The ORDs are based on the detector sampling distribution under the null hypothesis of no response to assess whether a given response is embedded in the electroencephalogram background (EEG). The performance of these detectors is strongly affected on both the signal-to-noise ratio (SNR) and the length of the electroencephalogram (EEG) signal. In this context, multivariate detectors are developed to increase the performance of the detection technique without increasing data recording time by using the information from more signals simultaneously. This work proposes three new bivariate detectors based on the normalization of the Global Spectral F-Test. The critical values ​​and detection probabilities for the new techniques were obtained theoretically and using a Monte Carlo simulation. The bivariate new detectors were next applied to the EEG from 10 subjects during intermittent, photic stimulation leading to an increase of up to 32.69% in mean detection rate in comparison with the univariate Global Spectral Beta-Test. The higher detection rate obtained with the proposed techniques without the need of increasing the number of data segments allows evoked responses to be detected faster, which might be useful in clinical practice.

The mean linear intercept ( $$L_{m}$$ L m ) has become a widespread parameter to evaluate lung structure, where its practical and simple implementation seems to justify its popularity. Nonetheless, a few issues arise when it comes to the development of test systems (grids), such as the multipurpose test system, used by point counting methods to estimate $$L_{m}$$ L m . This paper purpose is to analyze the relationship of the length of the line segments present on the multipurpose grid, and their distribution on it, with the $$L_{m}$$ L m in the lung parenchyma. Six male SAM-P/8 mice 10-months-old were used to compare the $$L_{m}$$ L m estimated with 149 different multipurpose grids, with line segments of different lengths and distributions, with the $$L_{m}$$ L m computed with the multipurpose grid called gold standard, which was developed following instructions in literature. The impact of the length of the line segments in the value of $$L_{m}$$ L m showed only significant differences when it was too shorter (3–31 pixels) or too long (273–299 pixels) than the estimated by the gold standard (127 pixels). It has also shown that depending on the position of the line segments in the grid, a small increase in its length (e.g., 1 pixel), may cause a drastically increase or decrease in the $$L_{m}$$ L m , particularly, with long line segments. This demonstrated that the length of a line segment in the multipurpose grid is important, but an adequate distribution of them should also be considered to avoid possible outliers.

Electrodermal response (EDR) is a skin conductance measurement used, for instance, to assess cognitive effort. It is commonly chosen for studying the psychophysiological of stress, especially with regard to the activity of the sympathetic branch of the autonomic nervous system (ANS). However, there is a lack of broadly consensual EDR indices, the current ones having limitations such as sensitivity to interindividual differences. The present work aims at testing two indices based on the amplitude of EDR, as to their ability to discriminate physiological responses elicited by different levels of difficulty of a customized puzzle video game. Thirty young healthy male subjects played 5-min sessions of easy, intermediate and difficult levels of the game, interleaved by 3-min resting periods, easy level first and randomized order afterwards. The proposed EDR indices discriminate game level better (66%) than classical indices (33%) such as amplitude and number of responses. The proposed indices showed high correlation among themselves (r = 0.92), which may indicate that both provide the same EDR information. However, only one of them presented fair correlation (r = 0.43) with one classical index. The present results support potential EDR indices that can contribute to solve classical EDR indices limitation.

This work presents a study of brain-computer interfaces (BCIs) to recognize motor imagery tasks, using the Riemannian covariance matrices to compute spatial features, and Linear Discriminant Analysis (LDA), Regularized Discriminant Analysis (RDA), and Support Vector Machine (SVM) as classifiers. These BCIs were evaluated through paired combination of four motor imagery tasks. The chosen BCI achieved promising results (accuracy (ACC) of 81.40%) using RDA, showing the best performance on Subject 1 (ACC = 94.45%). This study is a first stage to obtain a BCI based on motor imagery, in order to increase the effectiveness of a neuro-rehabilitation system using upper-limb robotic exoskeleton.

The paper presents the proposal of an electrocardiogram (ECG) compressor based on two-dimensional coding. The signal is pre-processed and the R waves detected, from these waves the signal is segmented and arranged in an N × M matrix, composed of M segments and N the maximum length between two R waves. Once this matrix is composed, the interpolation process is performed so that all segments M have the same size N. With the interpolated matrix a pre-processing is applied to improve the two-dimensional correlation of the matrix. This is done by rearranging the segments so that the correlation of the segments be maximized. Some widely-adopted image compression algorithm is used to reduce the volume of data. In the present work, the intra mode of the High Efficiency Video Coding (HEVC) is applied to encode real ECG signals obtained through the MIT-BIH Arrhythmia Database. These signals impose a greater challenge for the proposed system. The application of this proposal demonstrates that the use of HEVC-intra image encoder is efficient in ECG compression. A quantitative performance evaluation was performed and compared to other results found in the literature.

Julia is a programming language for numerical computing aiming at the combination of the usability features of a high level programming language with a good execution performance, allowing users to prototype and deploy their application using the same code. In this work, we evaluated the use of Julia for processing ultrasound signals and generation of a B-Mode image, being the input the raw signal acquired with an Ultrasonix platform using the Texo toolbox. We present the acquisition process using Texo and the signal processing chain implemented using Julia that encompassed: (1) reading data from files; (2) channel summation to obtain the scanlines (RF signal); (3) IQ demodulation; (4) envelope detection and logarithmic compression. We present the comparison of the execution time and the output image obtained with both Julia code and MATLAB®. In this particular application, we achieved a speedup of 2.7 times using Julia, but if we take into account only the ultrasound-related operations (channel summation to logarithmic compression), there is no speed up (0.56), i.e. MATLAB® code is faster. Besides that, the frame rate we achieved using Julia was about 7 frames per second. Based on these values, we conclude that it is not yet an alternative for a real-time medical ultrasound imaging system, but it can be an alternative for MATLAB® when performing simulations.

The diagnosis and evaluation of Parkinson’s disease (PD) is a task that has been performed through clinical evaluation and subjective scales. Over the years several studies have reported alternative technologies with the purpose of making the follow-up of PD more objective. Usually, in the objective evaluation, inertial sensors are employed for recording movement. A major challenge that exists in the area is related to the monitoring of the technological horizon, to identify and incorporate new technologies that can be used for the evaluation of PD. In this perspective, it was proposed in this research the evaluation of non-contact capacitive sensors to record four motor activities of the hand and wrist (i.e., radial and ulnar deviation, flexion and extension). To accomplish this, it was conduced a comparison between non-contact capacitive sensor and gyroscope. In this study, a correlation analysis in time and frequency domain were carried out. Experimental data were collected from three neurologically healthy individuals. The use of non-contact capacitive sensors introduces an innovative way to measure information from people with PD.

This paper presents the development of a simulator for EEG event-related potentials (ERPs) that uses as input parameter essential features that make up the signal, including amplitude, latency, wave width, jitter and signal-to-noise ratio. A database of auditory oddball stimuli signals was used to obtain the main parameters for the ERP simulator. The average of the signals in 9 electrodes was extracted to feed the data into the simulator and the synthetic output signal compared with real data. The results show good similarities between real and synthetic signals. A correlation of 67.4% between the average of the real and synthetic signals was observed. The simulator shows good potential as a tool to generate synthetic ERPs to aid and validate the development of techniques applied to EEG signal processing.

Colors have an important role in human perception. They imply feelings like urgency (red), productivity (yellow, green), restful (blue) and other sensations. Researchers have been investigating color impact in interface design. Some authors studied color influence in the brain waves aiming to verify changes of humor and/or performance while using an interface. The purpose of this study is to verify the difference between colors in brain response, human perception and performance while playing a game that was designed with a combination of fifteen rectangles filled with complementary colors and a single one filled with a different color. Ten volunteers performed the experiment where they were advised to identify the different color target. The time of performance and the EEG were recorded, and then the power spectral density was extracted and analyzed. Results showed an increase of alpha band in prefrontal, visual and primary motor cortex. The brain activity in the right hemisphere was greater than in the left hemisphere during the experiment. However, the comparison of brain response among colors combination presented no visual or significant difference. Moreover, data time analysis showed that the red-cyan-magenta scenario was the slowest one and the blue-yellow-red was the fastest, although there were no statistically significant between them.

An algorithm for automated placement of regions of interest (ROI) in Seed Based Correlation (SBC) data analysis for resting-state functional Magnetic Resonance Imaging (rs-fMRI) is presented in this paper. The sensorimotor network was used for testing and validation. Most of the available literature shows the use of manual seed selection in order to find the Resting-State Networks (RSNs). Typically, a seed is placed in the most preserved side of brain and its functional connectivity (correlation) with the contra-lateral hemisphere allows the identification of the network within the lesioned side of the brain. The manual placement of the seeds is usually a laborious task and prone to human error. The developed algorithm was based on the automated spatial registration of an atlas to the space of the patient’s brain: Anatomical (HarvardOxford) and functional (Brodmann Areas) atlas. Regions of interest representing the sensorimotor networks were used as seeds. FMRI data from 8 healthy volunteers were used to assess its validation. These data included a finger-tapping task and a resting-state protocol. The extracted sensorimotor RSNs derived from the automated procedure were compared to the task-based fMRI maps and RSNs extracted from SBC with manual ROI placement. Preliminary results show a good level of similarity between seed-based and task-based motor network maps, except in one case in which the patterns did not match. This technique shows potential to be used in clinical application due to the automated nature of the data processing as well as the ease for patients to perform the exam.

Congenital Zika virus (ZIKV) infection can cause abnormalities in the central nervous system with several levels of deficiency. The pathophysiology of this disorder has not yet been fully clarified, but neuroimaging studies have shown significant brainstem hypoplasia. Therefore, an unknown and possible increased risk of late-onset hearing loss need to be analyzed in these patients. We aimed to evaluate the brainstem evoked potential responses in children with microcephaly due to ZIKV congenital syndrome. Four children at ages 1–2.5 years underwent synchronous repeated stimulation of 80 dBNA to analyze the absolute latencies of waves I, III and V and the interpeak latencies I–III, III–V and I–V. The mean absolute wave latencies of children with microcephaly were similar to expected for healthy children of the same age. These wave latencies suggest integrity of the retrocochlear pathway, despite the brainstem hypoplasia. However, a regular follow-up is indicated since there is a risk of late-onset of hearing loss.

Auditory Brain Machine Interfaces is normally used to support people with severe neurofunctional disabilities. Great part of the auditory interfaces is designed with binary response, presenting to the subject a sequence of deviant and standard tones. Intending to increase the classes of the system, this article aims to study the feasibility of using spatially distributed sounds for enable interfaces with more than two control options. In the tests, the volunteers were exposed to virtualized sounds from the 0°, 60°, 90°, 130° and 180° directions and the electroencephalographic signal (EEG) was measured. Support Vector Machine was employed to classify the different cortical responses resulting from distributed sounds, presenting an average performance of 73.39%. This study confirms that there are significant differences in the auditory evoked potential between stimuli coming from five different locations and that it is possible to use sound virtualization to increase the amount of degrees of classes of a brain machine interface.

Changes in chloride homeostasis are associated with epilepsy. High intracellular levels of this ion promote increased neuronal excitability. It has been observed that the control of transmembrane fluxes of chloride ions involves the operation of channels, pumps and transporters, mechanisms that do not depend directly on the synaptic circuitry. However, there are still differences regarding the modulation of chloride during epileptiform activities. Thus, the present work investigated the extracellular depletion of chloride ions in the non-synaptic epileptiform activities (AENS) induced in hippocampal slices of Wistar rats by means of extracellular electrophysiological records in the dentate gyrus. The low extracellular chloride (7 mM) maneuver caused an increase in the frequency of firing in the first few minutes of perfusion, followed by changes in the extracellular electrical potential morphology. The analysis of the intrinsic optical signal (IOS) also showed a reduction of light transmittance during the maneuver. Changes in extracellular electrical potential were attributed to the possible occurrence of intracellular alkalosis during perfusion with low [Cl−]o, with consequent development of a neuronal hyperexcitability.

Cation-chloride cotransporters (CCC’s) have a direct effect on the regulation of intracellular chloride concentration ([Cl−]i) and neuronal excitability. Several studies propose the blockade of CCC’s as the main antiepileptic action of the diuretic furosemide (FUR). The aim of this study was to evaluate the effect of bumetanide (12.5 μM), VU0463271 (VU) (10 μM) and FUR (5 mM) cotransporter blockers on non-synaptic epileptiform activities (NEAs) by means of extracellular records in the dentate gyrus. Significant reduction in time between events occurred during infusion with the BUM, VU and BUM + VU inhibitors. Conversely, infusion with FUR promoted blockade of activities. Therefore, the blockade exerted by this diuretic does not result from its action on cotransporters.

When nonsynaptic epileptiform activity (NSEA) induced in the dentate gyrus of hippocampus slices (HS) are recorded, the transition to spreading depression (SD) can be seen. During this transition, the interplay of the different ionic species involved and the dynamic mechanisms of action responsible for the ionic homeostasis are too complex to be only experimentally accessed. Thus, we investigated the mechanisms responsible for this transition through the correlation between experimental findings and computational simulations. NSEA were experimentally induced in rat HS in interface chamber. When the slices were perfused with high-potassium (10 mM) and zero-added calcium, the NSEA appeared spontaneously after 1 h. The extracellular potential was recorded in the granule layer. The model describes the electrodiffusion processes of the ionic current through the neuronal and glial membranes and along the extracellular space. Results suggested that the interplay between the influx of Na+ through the channels and the Na+/K+ pump electrogenic currents sustains the NSEA and the transition to SD as well. According to the simulations, the transition from NSEA to SD in particular should occur when there is a failure of the energy metabolism, making the production of ATP insufficient for the maintenance of the Na/K pump activity.

Non-synaptic epileptiform activities (NSEA) are brain phenomena that involve intense neuronal activities and, consequently, change the energy metabolism responsible for the production of ATP and lactate. On the other hand, it has been observed that maneuvers of interference on mechanisms involved in metabolism can affect the epileptiform activities. The aim of the present work was to investigate, using mathematical modeling and computational simulations, the behavior of the energy metabolism responsible for the production of ATP and lactate, during NSEA. The mathematical model used to simulate NSEA describes electrochemical mechanisms of the dentate gyrus of the rat hippocampus. In this model, a mathematical description of the reactions involved in energy metabolism responsible for the production of ATP and lactate, in neurons and glial cells, was included. Investigating how neuronal and glial energy metabolism are able to maintain the level of ATP during NSEA, it was possible to interpret how it is possible to suppress NSEA by reducing the rate of glucose consumption, which characterizes the effect of ketogenic diets that have been used for the treatment of epilepsies.

The use of applications and systems controlled by Brain Computer Interfaces (BCI) has been growing in the recent years. Steady-State Visual Evoked Potentials (SSVEPs)-BCI systems are a promising field which consists in using brain response to any visual stimuli flickering at a certain frequency as input to an interface. The aim of this study is to evaluate the best positioning of visual targets, between cross and square arrangements, flickering at different frequencies (6.66, 8.57, 10, 11.9 and 15 Hz) to be used in a SSVEP-BCI system. Four participants were positioned, individually, in front of a LCD screen to perform the experiment. The Welch’s Power Spectral Density (PSD) was extracted from the signal and used to select 9 optimal electroencephalogram (EEG) channels (O1, O2, Oz, PO5, PO6, PO4, PO3, PO7 and PO8) through a Z-Score statistical analysis. A Fast Fourier Transform (FFT) was performed as signal feature extraction and used as input for a classifier. Using a One-Against-All (OAA) Support Vector Machine (SVM) classifier, a 96.66% accuracy was obtained for the cross interface and an 85.88% accuracy for the square layout. Thus, we concluded that the best configuration is the cross arrangement. In addition, the best classification was obtained for the stimulus at 15 Hz.

This study explored the differences in the functional connectivity after spinal cord injury (SCI). We studied 04 SCI subjects and 04 healthy non-SCI controls. All participants underwent structural and functional MRI on a 1.5-T magnetic resonance system while executing specific motor related tasks. Functional connectivity (FC) analysis was performed using SEED-to-Voxel correlation. The supplementary motor area and the precentral and postcentral gyri were chosen as references for FC evaluation. Results showed that the control group had additional reinforced connections in relation to the SCI group. In general, there was no reinforcement of connections in common regions between the groups. The areas whose connections were more strengthened for SCI were usually related to attention and concentration, whereas for the control group reinforcement occurred more expressively in regions directly related to the execution, planning and motor control. The results found in our study support that SCI causes a great impact in the cortical reorganization, especially in motor related regions.

Communication between robotic devices and the brain became possible due to the advent of Brain Machine Interfaces (BMI). In this context, the Openvibe (OV) software performs brain signal processing in real time. OV is an open source platform which provides tools for visualization and processing of electroencephalographic (EEG) signals. It is possible to communicate OV with external devices for BMI applications with Virtual Reality Peripheral Network (VRPN), a device-independent network-based protocol developed to interface application programs to physical devices such as ArduinoⓇ boards. In this way, data transmission between OV and Arduino enables the development of BMI projects like activation of prostheses and orthoses via brain activity. Thus, we present a protocol to send keyboard commands from OV to turn a LED on using an ArduinoⓇ Uno and a C ++ program to link the OV to the board in order to test communication. Later we show a BMI application to drive an upper-limb prostheses. Although simple, the paradigm described may work as a guideline for communication of brain signals to external devices such as hand orthosis and prosthesis.

Spinal cord stimulation has become a widely used and efficient alternative for the management of neurological disorders such as refractory chronic pain. The implanted devices likely induce activation of microglia, the cells responsible for the initiation of inflammatory response in the central nervous system. However, so far there are no available data on the microglial response following spinal cord epidural implants. This study intended to characterize the acute microglial response after spinal cord electrode implantation. To evaluate the acute response, a custom-made flat bipolar platinum electrode was implanted in the epidural space under the thoracic vertebra 4 (T4) in Wistar rats. Two days after implantation, morphological changes of microglia were evaluated by immunohistochemistry staining for ionized calcium-binding adaptor protein-1 (IBA-1) in spinal cord sections. Substantial loss of microglia ramification was found throughout the spinal cord at the implant site (T4). In contrast, microglia was not activated in areas distant from the implant such as cervical vertebra 4 (C4) and thoracic vertebra 11 (T11). This result interestingly demonstrates that semi-invasive implants in the spinal cord are able to induce the activation of microglial cells at the implant region. This work is the first step towards understanding the impact of epidural implants in spinal cord tissue.

It is well known that light touch (LT) of a stable surface with a fingertip reduces postural sway, but its mechanisms are still being studied. Generally, it is accepted that feedback provided by muscle afferents related to a finger used for LT helps decrease postural sway in standing subjects. Eleven participants stood upright on a foam pad set on a force plate with eyes closed. The experimental conditions involved two different finger positions, P1: included sensory information from the fingertip muscle flexors; P2: had no information from either fingertip muscle flexors or extensors. In the control condition, the participants kept the same stance, with no finger touch (NT). The stabilogram area (estimated from center of pressure (COP) signals measured by a force plate) was used as a quantifier of postural sway. Results showed that LT decreased COP area in comparison to NT. The elimination of feedback from the touching finger muscle afferents (P2 condition) induced similar reductions in postural sway as in P1. These results indicate that muscle afferent input (activated by movement of the distal phalanx of finger) is not able to generate an enhanced overall sensory feedback so as to induce a more pronounced decrease in postural stability as compared to the condition in which cutaneous fingertip afferents act alone.

During epileptic seizures an extracellular increase of K+ and decrease of Ca++ occur at the extracellular space. Under these circumstances, synaptic transmission is blocked and the seizures are sustained by non-synaptic mechanisms. Therefore, the search for antiepileptic drugs that act on non-synaptic targets can be considered as a powerful strategy in the control of seizures refractory to available drugs. There is evidence that integrins participate in epileptogenesis and therefore constitute a non-synaptic anti-epileptogenic target. In the present work, we investigated the effect of disintegrin leucurogin (LEUC) on non-synaptic epileptiform activities (NEAs) induced in hippocampal slices of rat. Records of the extracellular potential and the intrinsic optical signal (IOS) performed on the granular layer of the dentate gyrus show that, at the 33 μM, LEUC is able to reversibly block induced activities. IOS shows that the blockage extends throughout the granular layer, revealing the reversible non-synaptic antiepileptic potential.

Spinal cord stimulation (SCS) is a neuromodulation technique well established for the treatment of chronic pain syndromes and has been shown to be useful to treat movement disorders. Although the epidural electrodes do not invade the central nervous system, the implantation surgery has inherent risks requiring well trained staff and precise procedures to avoid spinal cord lesions. The major problem faced in experimental SCS device implantation in rats is the difficult access to the epidural area, making essential the development of a device that promotes a best column positioning and facilitates the fixation of the animal for spinal access. Here, we developed the SciTable, a spinal cord implantation table printed using Polylactic Acid (PLA). The surgery performance of three well trained surgeons during electrode implantation in rats showed significant improvement on animal stability, epidural area access and surgery time when the SciTable was used. Altogether, the results indicate that the use of the SciTable optimized the entire surgical procedure, which may positively impact animal survival and facilitate training of inexperienced surgeons. The continuous optimization of animal surgery is essential to safeguard animal welfare and to promote a faster development of the neuromodulation and neurosurgery fields.

Clinical and experimental data have indicated lesions in the amygdaloid nuclei of individuals with epilepsy as indicators of increased risk of sudden death. Among the several factors that are related to this risk, the most worrying is the high refractoriness of the anti-epileptogenic drugs available. These drugs target the inhibitory and excitatory processes of the synaptic circuitry. However, it is known that the full performance of synaptic processes depends on ionic homeostasis, controlled by mechanisms such as: enzymes, sodium/potassium pump, cotransporters, ion exchangers and extra-synaptic channels. In this sense, the present work investigated the expression of mechanisms responsible for ionic homeostasis, such as the cotransporter NKCC1, KCC2 and the Na+/K+ ATPase pump, in the amygdaloid nuclei of rats submitted to the status epilepticus (SE) by pilocarpine injection. The results showed clear lesions in various amygdaloid nuclei and variations in KCC2 and Na+/K+ ATPase expression. The observed changes suggest an imbalance in the control of inhibitory and excitatory processes, which modulate the synchronous activities of the amygdala, such as cardiorespiratory functions, evidencing a possible biomarker of the increase in the risk of sudden death in epilepsy (SUDEP) and revealing possible targets for its prevention.

Non-synaptic mechanisms have been related to the onset of epileptic seizures and brain damage caused by alcohol intoxication. To understand this relationship, adult male Wistar rats, between 300 and 350 g, were submitted to the binge drinking alcohol protocol, receiving ethanol every 8 h for 4 consecutive days intragastrically. Part of the animals developed status epilepticus about 12 h after the last dose of ethanol. Changes in the immunoreactivity of the cation-chloride co-transporters (NKCC1 and KCC2), Na+/K+-ATPase enzyme and astrocytic reactivity (GFAP expression) of alcoholic animals with and without seizures and control animals were investigated. Immunohistochemical analyzes were compared to electrophysiological recordings of non-synaptic epileptiform activity induced in hippocampal slices. The results show that the periodic and abusive use of alcohol is associated with imbalances of the ionic homeostasis in the brain, leading to serious osmotic variations related to neuronal and glial swelling, with concomitant cellular loss, which hinders synchronization processes, especially after status epilepticus.

Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technique with the ability to modulate corticomotor excitability and spinal reflexes. The aim of this study was to investigate what kind of tDCS protocols are used in rehabilitation of patients affected by Parkinson’s disease (PD) and with what kind of goal. It was performed a bibliometric review which analyzed: 52 publications, between 2010 and 2017, indexed on Web of Science, Scopus and IEEE database. 2017 was the year with the greatest number of publications and the predominant language was English. Italy was the country with the highest number of articles published. In relation to the most studied objective, the use of tDCS on balance and gait in PD was noted; the most used protocol was related to the placement of the anode electrode positioned on the dorsolateral prefrontal cortex (DLPFC) on the left and the cathode electrode was positioned over the right supraorbital area using the 10–20% method, with an intensity of 2 mA for 20 min. It was concluded that tDCS can have significant effects on balance and gait rehabilitation in patients with PD and that the protocols analyzed provide a favorable safety profile, tolerability, applicability and cost-effectiveness compared to other treatment techniques.

Optimal sensorimotor control is a complex task that is beautifully performed by the Central Nervous System (CNS). A great part of our control scheme is related to our capacity of learning novel skills. Theories of sensorimotor control have proposed the use of internal models by the CNS as a way to improve control when facing uncertainties, perturbations and to overcome slow feedback. In this paper, we investigated the dynamics of learning an internal model of a novel sensorimotor task during trials with and without visual feedback. In order to do so, 5 participants performed a sensorimotor task that translated force into positions of a cursor on the screen. We observed that performance gradually improved during training in both sensory modalities (correlation between learning curves was 0.89). However, the absence of visual feedback is detrimental to performance and somatosensory feedback alone was not sufficient to match performance when visual feedback is available. This suggests that error-signals given in visual coordinates are necessary to calibrate the internal model being learned.

New technologies have been developed with the objective of reducing adiposities in a less invasive way, among them is the selective cooling technique, cryolipolysis. The objective of this study was to perform an analysis of the body composition of women submitted to cryolipolysis using values provided by Dual-Energy X-Ray Absorptiometry (DXA). This was a longitudinal study, with 53 women, aged 20–35 years, and with localized abdominal adiposity. Mass, stature, abdominal adipometry and DXA scanning were verified before and 60 days after the application of technique. Women were divided into four groups, where they received different combinations of time and temperature. It was observed that all the groups studied showed reduction in adipometry, corroborating with the reduction of adiposity verified by DXA. In this study, cryolipolysis was effective for reducing abdominal adiposity.

Objective: To investigate the patterns of hospital births in Brazil 2013–2015 according to the birth group classification proposed by Robson. Methods: Data were obtained from the SINASC-DATASUS database and used to group pregnant women relatively to Robson’s classification, which is based on information concerning pregnancy and delivery. Results: Of the total 8,440,712 live births in Brazil, 2013–2015; 6,704,830 records could be used. Most pregnancies were classified in groups 5, 2 and 3; and 56.2% of the deliveries were c-sections. The groups 1, 2 and 5 contribute to two-thirds od the overall caesarean section rate. Vaginal births were higher in the Robson’s groups 3, 4 and 1. Conclusion: There are indications that non-clinical factors had an influence over the decision for cesarean sections in Brazil, 2013–2015.

This work presents a computerized method developed to teach the Brazilian sign language in a playful way for those people who have difficulty or want to learn a little about it. Concepts and contents present in the educational and entertainment games, concepts and techniques of the Software Engineering and the complexity of the sign language were taken into consideration to codify the challenges, which allow teaching the sign language alphabet, including the questions and symbols most used. Usability tests were carried out with different professionals. The System Usability Scale (SUS) was applied to 16 volunteers that evaluated the ease of learning, memorization, efficiency, risk of errors, runtime and user’s level of satisfaction. The computerized method obtained a score of 75.46 on SUS scale, for each one of the quality components assessed by the volunteers. The computerized method has user-friendly interface and presents potential to aid the teaching and learning process of sign language.

The constant evolution in the field of Biomedical Engineering requires betterment resources of bioelectrical signal analysis for Biomedical Engineering student demonstration. This project aims to develop a didactic platform intended to bioelectrical signals acquisition and processing. To perform this task, a specific hardware was used to the acquisition of bioelectrical signals whose characteristics are their versatility, be an open-source and be a low-cost device. Among the available options, it was selected the hardware called BITalino. To verify the efficiency of this hardware a characterization process was carried out, which consisted of analyzing the accuracy, frequency response and noise level associated with the acquisition of three types of bioelectrical signals (EMG, ECG, EEG). This work developed an interface for acquisition and conditioning of bioelectrical signals, based on LabVIEW (National Instruments®) software environment. The interface development process was divided into 3 parts: communication interface with the device; data conversion process and definition of signal conditioning processes. The project´s focus was accomplished with the development of a didactic platform, which the student can add noise to the signal, choose filters and perform frequency analysis, helping them to assimilate all the signal conditioning process.

The synthesis of an electromechanical model for 500 kHz and 1 MHz piezoelectric resonators used in low cost ultrasound transducer was performed. In the electromechanical simulations were considered the losses involved in the piezoelectric phenomenon and the efficiency in the transformation from mechanical to electrical energy and vice versa. An optimization algorithm was implemented and used to compare the proposed model with the electrical impedance curves obtained experimentally for two pure piezoelectric ceramics of PZT and two piezocomposites made from them. The results show the model predicts correctly the electrical impedance behavior and the mechanical and electrical properties of both piezoceramic and piezocomposites, as long as the losses are considered.

The accumulated fat in the abdomen can cause metabolic problems, cardiovascular and reduced quality of life and mental health. In an attempt to reduce this type of fat, less invasive procedures have been used, among these, the selective cooling technique, known as cryolipolysis. The aim of this study was to evaluate the reduction that can be obtained with a session of cryolipolysis in the abdominal region in young women. A total of 14 women, aged 20–35 years, with abdominal fat and BMI from normal to overweight participated in this study. The evaluation included abdominal perimetry, adipometry, and DXA. They received a session in the lower abdominal region with a protocol of 70 min, −8 °C and whit suction of 230 mbar to optimize contact. They were reassessed 60 days after the procedure. As a result, significant reduction value in DXA, adipometry, and perimetry in the lower abdomen (p < 0.05). Evidence that cryolipolysis may bring a considerable reduction of localized adiposity in the lower abdomen.

This study presents an approach for the definition of weight restrictions in Data Envelopment Analysis (DEA). To this end, the results of a Canonical Correlation Analysis (CCA) developed with the same DEA variables are used as restrictions for DEA weight calculation. Thus, in this approach the limits of the Wong-Beasley DEA method are chosen without interference from a decision maker. As an example, weight restrictions for a DEA model (Constant Returns to Scale (CRS)—model) were obtained through the Wong—Beasley method, which was applied to a dataset of 12 general hospitals in the city of Rio de Janeiro—Brazil, 2016. The DEA had as inputs the variables number of deaths and days of stay; and as outputs number of admissions and number of beds, and the rankings of a “restricted” and an “unrestricted” model were compared by a Spearman correlation procedure. The CCA had R = 0.92; and the Spearman correlation between methods was 0.82; p < 0.01. No “zero weight” coefficient was obtained by the new procedure. In conclusion, a better discrimination was achieved, and a problem that would have arisen in the classical CRS implementation (variables receiving zero weight) was avoided, allowing for a better classification procedure for the evaluated units.

The 5-stage structured population model proposed by Schechtman and Souza [1] was modified to comprise insecticide resistance as a recessive trait and allow for additional death rates at the larval and imago stages. Insecticide resistance was assessed by evaluating the total fraction of the allele for resistance present in the population. A very intense insecticide application policy was investigated. It comprised 18 blocks of applications of adult insecticide, with a resting interval of 2 days between any two consecutive block of applications, during the summer season for a period of a 100 years. Each block of application consisted of two daily applications of insecticide for 5 consecutive days. Insecticide resistance as expressed by an increase in the fraction of the allele for resistance occurred for this very intensive policy. The resistance allele, which started at a frequency of 2%, got fixated at 100% after the insecticide application period.

Studies of isotope analysis methods have grown in the past years, and they have been used as a tool to determine the geographic origin of human remains and to aid in the forensic identification, when DNA analyses and other standard procedures, such as dental records and fingerprints, fail or cannot be used. Isotope analyses may be useful in human identification and to determine geolocation. Isotope analyses of strontium, carbon, oxygen, nitrogen and lead have been used to archeologic and forensic investigations. The dental enamel is the chosen material, for its mineral composition and for it is less prone to environmental exchange (diagenesis). It is possible to perform a bioanthropological analysis of the teeth, due to its characteristic of recording, during its formation, and permanently, physical-chemical stresses, food records related to the consumption of food and water. The teeth have high resistance to the destructive effects of putrefaction and external agents, and due to the presence of high content of hydroxyapatite present in the dental enamel, being considered the hardest tissue of the human body, material that allows an analysis of a long period of the time after death of the individual. A database search was conducted between 1996 and 2017.

The dielectric properties of dry and wet palmar skin of the hand are presented here. The knowledge of these properties especially at frequencies below 100 Hz is important for dosimetry purposes. However, there are few empirical data available at these frequencies and they are contradictory. Electrodes made of carbon fibers were fabricated. They are concentric with an inner electrode and an outer ring one. Impedance measurements were collected from 1 Hz to 1 MHz on wet and dry palmar skin of the hand, then permittivity and conductivity were calculated. Permittivity values ranged from 1,912 to 6.6 × 106 and from 2,358 to 34.4 × 106 for dry and wet skin, respectively. Conductivity values ranged from 0.36 to 0.1 mS/m and from 2.4 to 0.13 mS/m for dry and wet skin, respectively. It was demonstrated that in the palm, the conductivity of the skin is higher and the permittivity much higher compared to the data given in the literature. This finding might be of interest because it increases the knowledge about skin dielectric properties importance when estimating the effects of low-frequency electromagnetic field exposure.

This paper aims to compare the so-called superficial heating methods used in Physical Therapy by comparing the therapeutic ultrasound and the infrared radiation, both considered superficial thermotherapeutic equipment. Thermotherapy is a therapeutic modality that promotes several health benefits. However, there are gaps in scientific knowledge regarding these techniques when applied to physical therapy and its clinical use. So, a Polyvinylchloride plastisol phantom was used to mimic the acoustic properties of soft tissues. The experimental setup was composed of a 3-MHz ultrasound equipment, and an infrared lamp. In the ultrasound protocol the nominal intensity of 1.0 W/cm2, continuous mode was applied during one minute (n = 10). In the infrared protocol, the phantom was positioned 30 cm apart from the infrared lamp during 15 min (n = 10). The infrared camera remained fixed in both protocols. After comparing the heating protocols, it was observed that the infrared radiation predominantly heated the upper surface of the phantom as it was directly irradiated by the lamp. The heat was probably transferred to the deeper and adjacent layers by conduction. The ultrasound radiation at 3 MHz, heated deeper sites of the phantom than the infrared radiation and had a predominantly heating pattern along the propagation axis of the ultrasonic beam, (i.e. in the central region of the phantom). The heating pattern contributes to understand the difference between both methods, and this work proposes a new approach to superficial heating studies related to physiotherapy equipment, an area that deserves more attention of the physical principles used in the clinic.

The Simple Reaction Time (SRT) consists in a response to a stimulus. It can be used to characterize the stages of motor control, which are: stimulus identification, response selection, and execution of movement. Slow reaction times can indicate the possibility of a neuromotor disability. Thus, several studies employ the measurement of SRT to assess the performance of subjects. In this context, this study, based in a single case, developed a protocol using wrist extension movement as the response to a visual stimulus. The different SRTs were measured using electromyography (EMG), a button, and a gyroscope. These measurements allowed to estimate the time interval between the distinct types of motor response. These results showed the possibility to identify the stages of motor control, which obey the following sequence: first, the electromyographic activity response, then, the start of the movement, and, finally, the conclusion of the movement. Therefore, this protocol can be used to help clinicians in the diagnosis and evaluation of diseases affecting the brain and muscles.

There are various elasticity modalities to obtain mechanical properties of human tissue. Measuring these properties are valuable in clinical diagnosis because it can lead to diagnose some diseases such as cancer and liver fibrosis. In recent years, an imaging technique called magneto-motive ultrasound (MMUS) imaging has been introduced as a technique to improve the sensitivity of ultrasound using magnetic nanoparticles (MNPs) as contrast agent. An external alternating magnetic field gradient is applied to induce a displacement in the soft tissue labeled with iron oxide nanoparticles and the resulting displacement in the medium is detected by ultrasound. In this study, a type of MMUS was used which is known as Shear Wave Dispersion Magnetomotive Ultrasound (SDMMUS). This modality, which is a novel elasticity method, estimates viscoelastic properties of a medium labeled with magnetic nanoparticles (Fe3O4) by generating shear waves created via an alternating magnetic field. Here, 2 different gelatin tissue-mimicking phantoms (4% of gelatin concentration) were made, in which including an inclusion of iron oxide nanoparticles with certain concentrations of 1.2% and 0.3%. The lowest possible concentration was 0.3% to observe shear wave (SW) and demonstrate a satisfactory contrast in SDMMUS.

Methods of assessment and monitoring of proprioceptive function have often been discussed. Most clinical proprioceptive evaluations are based on categorical or ordinal results and are not sensitive to changes and subtle deficits. This study aimed to propose a quantitative proprioceptive assessment protocol based on three-dimensional inertial sensors, easily adapted for clinical practice. Eighteen healthy subjects participated in the study, divided into age groups. Five inertial sensors were coupled to the upper limbs, and active movements were made to reach a reference point. The subject was blindfolded and was then instructed to replicate the move with the contralateral limb. We assessed 02 shoulder joint angulations and 02 angles for elbow joint. The results indicated that the equipment can generate quantitative measures more precise than the commonly used clinical tests.

We evaluated the effect of exposition to asbestos in the complexity of the respiratory system investigating the airflow pattern sampling entropy (SamEnV′), recurrence period density entropy (RPDEV′) and variability (SDV′). A non-invasive and simple protocol for assessing respiratory mechanics during spontaneous breathing was used in a group of 30 controls and 29 asbestos-exposed patients. Exposition resulted in a significant reduction in the RPDEV′ (p < 0.001) and an increase in SDV′ (p < 0.001). These results suggest that the airflow pattern becomes less complex in exposed patients, which may explain the reduction in respiratory systems’ adaptability to daily life activities. The influence of smoking was also investigated in exposed patients, which resulted in a significant reduction in SamEnV′ (p < 0.03). This suggests a further abnormal effect in smokers. Diagnostic accuracy evaluations indicate that RPDEV′ and SDV′may contribute to the diagnosis of respiratory abnormalities in asbestos-exposed patients with accuracies of 76.9 and 78.8%, respectively. As measuring airflow pattern complexity during tidal breathing is simple method, requiring only the passive cooperation, this technique may constitute a significant clinical contribution by providing novel respiratory biomarkers to facilitate the diagnosis of respiratory abnormalities these patients.

Prostate cancer is one of the most frequent among cancers in men and among the treatments most indicated for the individuals affected by this pathology, is the radiotherapy. In radiotherapy is used ionizing radiation that can be applied in teletherapy, brachytherapy and radiosurgery. Periodically, dosimetric tests are performed for quality control aiming specifically, to deliver the correct dose to the patient. In this research work, a phantom was developed, that is, an anthropomorphic prostate simulator made in a 3D printer, associated to the application of the Fricke gel dosimeter, for the purpose of performing a dosimetry test for 3D dose evaluation, with the application of dose in teletherapy through linear accelerator. The purpose of this study is to assist in the quality control of radiotherapy services, which are applied, essentially, by professionals involved, medical physicists and professionals in radiological techniques, in the planning and application of quality control of radiotherapy services.

In this paper, machine learning algorithms were applied for the diagnosis of cardiovascular diseases with the objective of proposing a tool for medical assistance. Artificial neural networks were used to classify patients as having or not having a cardiovascular disease, and a decision support system proposes to the clinician explanations of why such classification was made. An Amazon EC2 cloud server was used to test hyperparameters combinations, and an optimal configuration was used in the training section. As a result, the network achieved a 90.74% recognition rate of the test data and presented a score of 0.91 in the recall, precision, and f-1 score metrics, and 0.94 in the precision-recall and ROC curves.

Ischemic stroke is a worldwilde health problem that justifies the need for investigations and for stroke animal models in determining new treatment and diagnostic imaging strategies Brain perfusion of a rat model for stroke was analyzed using a functional micro-ultrasound (fMUS) imaging technique associated with ultrasound contrast agent (UCA) bolus-tracking imaging sequence to quantify the hemodynamic parameters. Stroke was previously induced occluding the left middle cerebral artery and then the rats (n = 3) had a cranial window opened in on both sides of the Bregma and contrast-enhanced ultrasound perfusion imaging were acquired from both sides. The non-affected contralateral ROIs served as reference. The measured perfusion parameters AUC (area under the curve) and Pi (perfusion index) were significantly lower in the stroke side when compared with control. This technique provided an effective tool to quantify the microcirculation of the rat cerebral cortex, using UCAs for an improved visualization of capillary tissue blood flow in real time, which facilitated discrimination of viable from necrotic tissue.

Phantoms are test-objects made of materials that mimic certain properties of biological tissues. The mimicked characteristics will vary according to the target tissue and the focus of the study in which it will be employed. Several materials have been studied to reproduce the acoustic properties of the tissues but most of them are not stable. This work aims to characterize the acoustic parameters of a solution of poly(acrylic acid) and a gel produced with this solution, enabling the elaboration of acoustic phantoms that simulate different tissues. Samples with a concentration of 0.5% w/v of this polymer were prepared. Through the technique of Transmission-Reception of acoustic signals it was possible to conclude that there was no significant variation of the ultrasonic pulse propagation velocity with the emission frequency variation. On the other side, there was a hange in the wave attenuation coefficient with increasing frequency, as expected. Other properties, such as density, acoustic impedance and volumetric compressibility were characterized. The values found in this study are within the range of interest for biological tissues and show that the Carbopol polymer is a promising material in the manufacture of ultrasonic phantoms.

Virtual Reality (VR) scenarios have been presented as an interesting therapeutic tool to train abilities in individuals with autism. These patients can present hyper- or hypo-sensibility to sensorial stimuli. VR developers can customize the environment to provide a more comfortable and adaptable place for these patients. However, some VR engines may be limited in this respect. Therefore, this work presents a comparison of seven engines that can be used to build games for individuals on the autism spectrum. It was considered not only the technical development requirements, but also functional requirements applicable to the autism scenario. As a result, the Unity engine showed to be the most appropriate tool.

Diffuse Optical Imaging (DOI) is a noninvasive technique that uses diffuse light in the spectral region of the visible and near infrared to measure the optical properties of biomedical tissues. The technique depends on the studied object, generally being a tissue with low absorption capacity in the NIR light; the technique is commonly applied in soft tissues such as tissues of breasts and brain. DOI has recently moved from being a theoretical model to a popular instrumentation in clinics and hospitals. This article aims to create an equipment of a diffuse optical imaging system by illumination in the near infrared spectral region, which could validate the diagnosis of pathologies in human skin and work as a vein viewer device. The prototype uses 850 nm (LED) arranged in a ring-shaped aluminum structure, with an ergonomic handle and a coupled CCD camera, whose capture radiation spectrum goes from visible to NIR. The system was designed in CAD 3D and plastic parts were 3D printed. Images were processed using several plugins of imageJ and OpenCV libraries.

This work proposed the diagnosis of iron deficiency anemia (IDA) in human blood caused by iron (Fe) deficiency, which is the most common nutritional deficiency, by means of Raman spectroscopy. Total blood samples from patients diagnosed with IDA, as well as from normal subjects (HbA) were obtained and submitted to Raman spectroscopy (830 nm, 150 mW, 400–1800 cm−1 spectral range, 2 cm−1 resolution) through a Raman probe and an aluminum sample holder. Then, the normalized spectra were submitted to discriminant analysis based on partial least squares (PLS-DA) and principal components analysis (PCA-DA). The comparison between the spectra of HbA and IDA showed very similar spectral features, and an exploratory analysis based on the scores and loadings of PCA showed that the spectral differences appear in the peaks at 971, 1237, 1374 and 1551 cm−1, which can be attributed to hemoglobin molecule. Discriminant analysis using PLS-DA and PCA-DA showed that the IDA spectra could be separated from the HbA spectra with 88.3% and 81.7% of accuracy, for the PLS and PCA respectively. It was concluded that the iron depletion of red blood cells can be identified by the Raman spectroscopy and these depletion could be used to accurately discriminate these IDA samples from the normal HbA.

Various disorders, such as disseminated intravascular coagulation, may increase or reduce the fibrinogen concentration in the blood with the consequence of modifying the viscoelastic properties, such as the shear modulus, µ, of the blood clots. Therefore, measuring µ for blood, or plasma, clots can become one way of determining the fibrinogen concentration. An ultrasonic system operating in pulse-echo mode (probing system) with nominal frequency of 5 MHz and another ultrasonic system based on impulsive acoustic radiation force (IARF) were used to quantify the time-changing shear modulus of a normal control plasma and of a plasma with fibrinogen lower concentration during plasma clotting. The IARF-based ultrasonic system transmitted bursts with frequency of 2.03406 MHz, 500 cycles and 1.249 Hz of pulse repetition frequency to generate an IARF on a glass sphere (3.0 mm in diameter and density of 2500 kg/m3) embedded in a plasma sample, causing a transient displacement that was monitored by a probing system. The employed ultrasonic method was able to quantify the shear modulus of normal control plasma and plasma with fibrinogen concentration of 0.8 g/L throughout the coagulation process. The results indicate that this method can be used clinically because it uses small amounts of plasma sample and has the ability to detect differences in µ as a function of fibrinogen concentrations.

Low-level laser (LLL) therapy promotes biostimulating effects in cell cultures growing in nutritional deficit. However, the effects of LLLs on tumor cell lines remain controversial. Studies indicate stimulatory, inhibitory or even no influence in this type of cells. Therefore, the aim of this study was to evaluate the influence of LLL irradiation on the cell viability (with and without nutritional deficit) of human melanoma SKMEL 37 cells and murine melanoma B16F10 cells using an infrared laser (λ = 780 nm) with different radiant exposures. The cell lines were subjected to the LLL 24 h after they were seeded in a 96-well plate at a density of 5 × 104 cells per well. The analysis of cell proliferation by mitochondrial activity occurred at intervals of 24 and 72 h after laser irradiation. At each time, culture medium was removed and 180 µL of PBS and 20 µL of MTT were added. The plates were incubated for 4 h and the absorbance was read in a microplate reader at 570 nm. Results showed a non-significant statistical difference among the groups for both cell lines regardless the nutritional medium. The metabolic pattern was similar among the groups. It is concluded that irradiation with 780 nm laser light at radiant exposures of 30, 90 and 150 J/cm2 and an output power of 40 mW does not promote cell proliferation on melanoma cell lines.

Electrochemical Impedance Spectroscopy (EIS) is a prevailing modern technique to depict electric properties of materials. The present work related to in exploring the exclusion zone properties by using theoretical equivalent model and measured data. The dielectrics properties of distilled water in contact with hydrophilic material revealed that at the interfaces metal, hydrophilic surface has a different time constant values. At the interfaces, water molecules are pointing as H-up and H-down either towards or away from the surface to form structures and layers closely packed with each other. The H-up and H-down have a significant difference in the work function with metal interaction. The data acquisition system uses LabVIEW and myDaq hardware, in integration with MATLAB via OPC communication. When comparing fitted model and equivalent circuit the equation system presents nonlinearity. A simple usage of a Levenberg-Marquardt algorithm to solve the system was not coherent with expected responses of the theoretical impedance system, motivation to make a process that quantifies MSE (Mean Squared Error) between fitted data and solved system. Promising results rises when applying nafion on both sides of electrodes at room temperature and at fusion. One can expect that with a low impedance less would be the disorder, which is the probability of having structured water.

Objective: To verify if there is difference in the postural control between the elderly practicing physical and the sedentary ones Through the stabilometry. Methods: A total of 62 elderly people were evaluated, of which 28 were physical activity practitioners and 34 were sedentary individuals aged 60 or over. In evaluation was performed by means of the stabilometry test in electronic baropodometer in four experimental conditions. Results: Sedentary patients had higher body mass index values. There were no significant differences between patients who practiced physical activity and sedentary ones in the stabilometry. The group of separated feet, open arms and closed eyes (EC4) presented higher values of ellipse área than open eyes (EC3). Conclusion: It is concluded that overweight contributes to the possibility of fall of the elderly, the physical activity influences in the postural control, and that the tests should be performed with the patient with open eyes for greater postural stability.

Orthopedic patients treated with external fixators need to manage such devices for adequate bone movement as well as wound care for several times a day for many weeks. The objective of this preliminary study was to evaluate the applicability of a smartphone application prototype used by orthopaedic physicians to assist patients during external fixator treatment. A presentation of the smartphone application prototype design was performed using screens displaying information, dates and times to start and finish the treatment, including sound and vibration alarms. After approval by ethical research committee and acceptance Informer Consent, questionnaire was performed to both groups: trainee orthopedic residents (n = 24) and 10-year qualified orthopedic surgeons (n = 32) to assess software acceptability and indication following project presentation. Acceptability and indication of the application prototype by professionals in the orthopedic field was high. Comparison between the two groups of orthopaedic physicians did not show a significant difference for any of the proposed questions regarding acceptability, expected patient adherence, benefit to patients and user-friendliness (p > 0.05). Most physicians regarded the application design useful, user friendly, with possible high indication and highly beneficial to patients, with no significant difference between the two groups for any of the questions (p > 0.05).

In this work, dispersive Raman spectroscopy has been employed as a rapid and nondestructive technique to detect the metabolites in urine of physical exercise practitioners before and after exercise. For so, urine samples from 14 men were obtained before and immediately after physical activities and submitted to Raman spectroscopy (830 nm excitation, 250 mW laser power, 20 s integration time). The Raman spectra of urine showed peaks related to urea, creatinine and phosphate. These metabolic biomarkers presented peaks with different intensities in the urine after exercises compared to before, measured by the intensity of selected peaks the Raman spectra, which means different concentrations after training. These peaks presented different intensity values for each subject before physical activity, also behaving differently compared to the post-training: some subjects presented increase while others decrease the intensity. Raman spectroscopy may allow the development of a rapid and non-destructive test for metabolic evaluation of the physical training in active subjects using urine samples, allowing nutrition adjustment with the sport’s performance.

Erbium lasers can be used to prevent dental caries, which has a high prevalence in the worldwide population. However, effective irradiation parameters for root dentin have not yet been determined using the Er,Cr:YSGG laser. Objective: this study evaluated the chemical, morphological and optical effects of Er,Cr:YSGG laser on root dentin when aimed at preventing root caries. Methodology: 75 bovine root dentin slabs were randomly distributed in 5 groups: G1-untreated; G2-treated with acidulated phosphate fluoride gel (APF-gel, [F−]= 1.23%); G3-Er,Cr:YSGG laser irradiation (2.78 µm, 60 µs, 6 J/cm2, 8,67 mJ/pulse, 0.25 W); G4-Laser irradiation + APF-gel application; G5-APF-gel application + Laser irradiation. The chemical and morphological evaluations were performed using Fourier transformed infrared spectroscopy and scanning electron microscopy, respectively. Afterwards, the samples were submitted to an 8-day pH-cycling model and the optical attenuation coefficient was evaluated by optical coherence tomography. The statistical analysis was performed considering the level of significance of 5%. Laser irradiation alone does not alter the dentin composition, but the previous application of APF-gel followed by laser irradiation significantly decreased the content of ν3ν4 carbonate of dentin. This treatment also promoted greater morphological alterations, such as ablation of the surface, when compared to the treatments alone. After demineralization, this treatment also presented the highest optical attenuation coefficient value when compared to the other treatments, indicating less demineralization of the samples. Conclusion: Er,Cr:YSGG laser presents potential for use in prevention of root dentin demineralization, and is more efficient when preceded by the application of APF.

Lung ultrasonography (LUS) is a diagnostic tool increasingly utilized in intensive care. Impedance difference between alveoli air and chest wall tissues impairs deep penetration of ultrasound signal, generating a horizontal hyperechoic artifact pattern, called A-lines. Atelectasis is air absence in a lung, making ultrasound signal easier to identify a heart rate pulsation in pleural line, so-called lung-pulse sign. M-mode allows objectification of this pattern. The aim of this study is to propose a LUS phantom that simulates both lung-pulse sign and A-lines artifacts. Polyvinyl chloride plasticizer (PVCP) was the base of thoracic wall phantom confection. A thin-walled silicone tube was inserted through the phantom and connected to a peristaltic pump, offering 84 cycles per minute rate to simulate heart activity. A commercial latex condom coated a polyurethane foam to simulate the pleura and lung parenchyma, respectively. Images were obtained on commercial clinical ultrasound equipment with the linear transducer, which was fixed with an articulated vise in a vertical position in contact with thoracic wall phantom. Lung phantom without air (withdrawal by syringe) was introduced into the thoracic phantom next to the silicone tube and to the wall where the transducer was positioned. A peristaltic pump was turned on and images started to be acquired. Subsequently, an experimental setup was reorganized for new images acquisition with lung phantom completely opened and motionless. In the results, first images replicated collapsed lung, with visible lung-pulse sign; and second setup mimicked opened lung A-lines, with suppression of lung-pulse.

Ozone therapy is a powerful technique applied for several purposes. One of these modalities is blood ozonation, a complex process that must have regard to the integrity and properties of blood components. In this work a protocol to in vitro blood ozonation applied to a blood bag was developed, using a venturi injector and a centrifugal pump in a closed circuit system. Our results showed that the protocol developed provide small variations of the hematological concentrations; allow us to infer that concentrations of the red and white blood cells and platelet series remain relatively stable during blood ozonation process on gas flux of 1/8 L/min and ozone concentration of 33 and 62 mg/L. This innovative protocol realized indirectly to the blood bag was the potential to minimize bacterial contamination of blood components in blood bank and realizes a high efficiency ozone-blood mass transfer in order to induce by ozone oxidative balance an indirect blood quality status.

Gold nanoshells (silica core/gold shell) have fascinating optical extinction spectrum within visible to near infrared range. In this work, we investigate the optical properties of Au nanoshells and evaluate the feasibility of their use on photodynamic therapy. Three dimensional finite element simulation and experimental analyses were explored on the assessment of the localized surface plasmon resonance spectrum and spatial distribution of the electromagnetic field enhancement near metallic nanoshells. In addition, the interaction of Au nanoshells with methylene blue (MB) photosensitizer was appraised, and 3.2—fold metal enhanced single oxygen generation was observed. Lastly, we investigate the effects of MB-mediated photoinactivation on Staphylococcus aureus assisted by gold nanoshells. The use of nanoshells on the photoinactivation procedure reduced 3× the required illumination time for total eradication of bacterial cells. Our results indicate that Au nanoshells are promising candidates to enhance the photodynamic effect on bacterial cells.

Dental erosion has been extensively studied as a risk factor for tooth loss or injure, and the early diagnosis of lesions is essential for avoiding greater damages. Optical Coherence Tomography (OCT) is a potential tool for early diagnosis of demineralization. In this study, this technique was used to analyze the optical changes of dentin samples irradiated with Nd:YAG laser using a black dentifrice as photoabsorber, then submitted to an erosive cycling. 75 slabs of bovine root dentin were randomized into 5 groups: G1—untreated; G2—treated with acidulated phosphate fluoride gel (APF-gel, [F-] = 1.23%, pH = 3.3–3.9); G3—irradiated with Nd:YAG laser (100 µs, 1064 nm, 0.6 W, 10 Hz) without photoabsorber; G4—irradiated with Nd:YAG laser using a coal paste as photoabsorber; G5—irradiated with Nd:YAG laser using a black dentifrice as photoabsorber. All samples were submitted to a 3-day erosive demineralization (Citric acid 1%, pH = 3.6, 5 min, 2×/day) under agitation, and remineralization (artificial saliva, pH = 7, 120 min) cycling. The samples were evaluated by OCT before treatments (baseline), after treatments and after erosive cycling. Optical attenuation coefficient (µ) was calculated using a Matlab routine, and the statistical analysis was performed (α = 0.05). It was observed a significant decrease on µ values after all treatments. Also, the µ values decreased after erosive cycling, except for the groups G3 and G5. It was concluded that OCT technique is capable to distinguish among sound, treated and demineralized dentin. As well, the black paste was efficient to act as a photoabsorber, helping the Nd:YAG laser to decrease dentin erosion.

The study of the tendons adaptation process to progressive overload is fundamental to design strategies directed to prevent injuries and rehabilitation. However, the patellar tendon (PT) shear modulus (µ) response to resistance training (RT) is still unknown. We used supersonic shearwave imaging (SSI) to evaluate the PT µ changes in response to an 8-week RT protocol in healthy young men. Ten healthy young males were included and submitted to an 8-week progressive RT. Knee extension torque (KT) at baseline and after 8-week to guarantee the RT efficacy. PT µ was assessed by SSI at baseline and at the end of the training protocol. KT exhibited statistical significant changes after the resistance training protocol (pre = 290 ± 45 and post = 330.5 ± 82.9, p = 0.005). No statistical significant changes in PT µ were observed after the eight weeks of RT (pre = 31.06±6.09 and post = 27.12 ± 8.51, p = 0.254). The present study showed that a progressive overload regimen consisting of 8-week RT was not able to promote PT µ assessed by SSI, even thought it was effective to promote gains in strength. Further investigation should be conducted with special attention to longer interventions and to possible PT structural changes set points.

The present work evaluated the effects of LED light therapy at 640 nm on healing the navels of newborns by using animal model. Fifty-seven neonatal calves were divided into two study groups. The members of the control group had the umbilical stump immersed in a solution of 10% tincture of iodine for 60 s, which was repeated every 24 h for three consecutive days. The members of the treated group received light doses of 7.2 J that were applied to four points evenly distributed around the insertion site of umbilical stump every 24 h for three consecutive days before the umbilical stumps were immersed in the iodine solution. The time, in days from birth, at which the umbilical stump fell off for each calf was noted. After the umbilical stump fell off, the remnant wound area was measured at different times after birth. Digital photographs with a dimensional reference were used for area measurements. The results showed that phototherapy hastened the fall-off of the umbilical stump, accelerated navel healing and reduced the mortality rate in newborn calves. We can anticipate that light treatment, as an afterbirth care, could also bring benefits to human neonates.

The diabetes mellitus (DM) is one of the most complex health problems worldwide, presenting high mortality and morbidity rates, in which, one of its most frequent pathologies is the diabetic foot. The diabetic foot occurs due to a metabolic disorder, leading wounds to take longer healing periods than expected, in most cases, resulting in ulcers and then to non-traumatic amputations. In this context, to support defective wound healing processes, the phototherapy assistance device RaphaⓇ is introduced, inducing—by means of photostimulation—the tissue neoformation and angiogenesis. This device applies a LED (Light-Emitting Diode with wavelengths of approximately 680 nm) matrix for low light intensity treatment, emitting luminous energy to injured regions. Furthermore, aiming a proper and efficient therapy, it is necessary to evaluate the LEDs distribution layout and the influence of different matrix dimensions in the output luminous emission. Therefore, an algorithm for simulation is implemented, using the software MATLAB® 2015a, The MathWorks, Inc., Natick, MA, applying the Lambert Law equation—that describes the light behavior in different materials. In sum, two different layouts are analyzed—the current rectangular form and a new hexagonal suggested arrangement, aiming an improved LED matrix layout with an optimized irradiance emission pattern.

The objective of this study was to determine parameters that better describe thermal models to separate people who present Raynaud’s phenomenon (RP) from those who do not. A descriptive exploratory study was conducted with 284 participants (primary RP = 14; secondary RP = 12; without diagnosis = 258) aged between 18 and 60 years, in the city of Curitiba, Brazil. A thermal camera (Fluke Ti400) was used to photograph skin temperature following a cold stress protocol. The hands were then immersed up to the carpal level in a container with water at 10 °C for 60 s. Images from both hands in six different moments were taken: (a) pre-stress to cold; (b) immediately after the cold related stress and every 5 min, until 20 min post-immersion. In order to find the best parameters to identify primary and secondary Raynaud, several features based on the temperature dissimilarities between fingers and hands were evaluated. Our results show that the dorsal-distal difference temperature (DDD) is well-known to separate individuals who do not have RP. However, primary and secondary Raynaud mix up, so no pattern can be possibly distinguished. In addition, the best result on the classification was achieved using the maximum temperature difference at 20 min and the maximum absolute temperature difference between hands at 15 min, reaching an accuracy of 80.56% ± 17.35%.

Virtual reality (VR) is a new approach and offers physiotherapy alternative way in the treatment of patients with pelvic floor (PF) disorders. It’s fundamental importance the study of the pelvic musculature quality in order to avoid or treat such disorders. This work proposes to define the anatomical region of the PF that provides good electromyographic (EMG) signal response, in a noninvasive way. In order to use this information as reference for application of surface electrodes, which will perform the skin/electrode interface with the hardware developed for signal acquisition. The hardware processes those signals that will be transmitted via Bluetooth to a mobile device with the Android operating system. The information received by the device feed VR games. Where commands in the game are based on the EMG signals levels, captured during contractions of the PF musculature. The experiment was carried out with 30 women, where average and peak information of the mean square root (MSR) of the EMG signals from the electrodes were collected when applied in the region three and nine hours of the perianal clock and in the region immediately below the labia majora, and from an intravaginal tube. The results indicate that when the electrodes are applied at the three and nine-hour perianal clock region, allow an equivalent evaluation, the intravaginal probe, of the quality of the pelvic floor musculature (PFM). Thus, allowing proportional biofeedback of the perineum muscle tone for the use of VR games in rehabilitation physiotherapy.

Introduction: The aim of the present study is to report normal reference values of nerve conduction studies (NCS) parameters for ulnar motor nerve, and investigate the effect of age, gender and anthropometrical factors height and body mass index (BMI) in the measures. Methods: Ulnar motor NCS were performed on 25 healthy volunteers aged 25.9 ± 4.2 years (mean ± SD), with exclusion criteria related to risk factors for neuropathies. The ulnar nerve was stimulated at the wrist (distal) and below the elbow (proximal); compound muscle action potential (CMAP) was recorded from the abductor digiti minimi. Reference data was estimated as a range of mean ± 2 SD and a range based on the 5th and 95th percentiles. The effect of covariates was evaluated with correlation coefficients and linear regression analysis. Gender comparisons were analyzed with unpaired t-test. Results and Discussion: At distal point the overall results were: amplitude of 9.1 ± 1.4 mV, latency of 2.4 ± 0.3 ms, CMAP duration of 6.4 ± 1.0 ms and area of 27.6 ± 4.8 µV s. For the proximal stimulus point: amplitude of 8.3 ± 1.8 mV, latency of 5.65 ± 0.6 ms, CMAP duration of 6.6 ± 1.1 ms and area of 25.8 ± 4.6 µV s. Motor nerve conduction velocity (MNCV) of 63.5 ± 6.4 m/s. Distal latency and MNCV were significantly (p < 0.01) correlated with height, accounting for 38% and 33.5% of the variability, respectively. Female subjects had smaller distal latency and greater MNCV than male subjects (p < 0.01); this can be attributed to significant (p < 0.01) height differences between gender. No significant correlation between NCS parameters to age and BMI was found. The results demonstrate that the effect of height need to be taken into account when deriving normal values of NCS for ulnar motor nerve.