Innovation in Medicine and Healthcare 2016

Nowadays, assistive technologies together with ubiquitous and pervasive computing are emerging as main alternative to help ageing population. In this respect, an important number of works have been carried out to improve the quality of life in elderly from a physical point of view. However, less efforts have been made in monitoring the mental and emotional states of the elderly. This work presents a non-linear model for discriminating different arousal levels through quadratic entropy and a decision tree-based algorithm. Two hundred and seventy eight EEG recordings lasting one minute each were used to train the proposed model. The recordings belong to the Dataset for Emotion Analysis using Physiological signals (DEAP). In agreement with the complexity and variability observed in other works, our results report a low quadratic entropy when subjects face high arousal stimuli. Finally, the model achieves a global performance around 70 % when discriminating between calm and excitement events.

This paper introduces a software program to recognise discrete emotions on an ageing adult from his/her physiological and psychological responses. This research considers the capacity from an audiovisual method to evoke different emotions and uses it to interpret and modulate basic emotion states. Different body sensors, in the case of physiological response, and a set of questionnaires, in the case of psychological responses, are selected to measure the power in causing fear, anger, disgust, sadness, amusement, affection and the neutral state, through a set of films used as an emotional induction method. The initial results suggest that it is possible to extract discrete values about positive and negative emotional states with films and to use these responses as keys to get emotion regulation.

In this paper a novel method to evaluate the quality of the voice signal is presented. Our novel hypothesis is that the first and second formants allow the estimation of the jaw-tongue dynamics. Once the velocity is computed, it is approximated by the Sigma-Lognormal model whose parameters enable to distinguish between normal and pathological voices. Three types of pathologies are used to test the method: Laryngeal Diseases, Parkinson and Amyotrophic Lateral Sclerosis. Preliminary results show that the novel features proposed are able to distinguish between parameters of normal and pathological voice. Moreover, it is also possible to discriminate between the three types of pathologies studied in this work.

Mental health related problems are responsible for great sorrow for patients and social surrounding involved. The costs for society are estimated to be 2.5 trillion dollar worldwide. More detailed data about the mental states and behaviour is becoming available due to technological developments, e.g. using Ecological Momentary Assessments. Unfortunately this wealth of data is not utilized: data-driven predictive models for short-term developments could contribute to more personalized interventions, but are rarely seen. In this paper we study how modern machine learning techniques can contribute to better models for predicting short-term mood in the context of depression. The models are based on data obtained from an experiment among 27 participants. During the study frequent mood assessments were performed and usage and sensor data of the mobile phone was recorded. Results show that much can be improved before fine-grained mood prediction is useful within E-health applications. Subsequently important next steps are identified.

Current refugee crisis within European Union (EU) challenges mental health care systems in each EU country. For ethnic minorities in EU access to mental health care is a problem due lack of clinicians who understand their language, culture and special needs. Linguistic, cultural and even racial differences between patient and provider can have an impact on the therapeutic alliance. Therefore communication between providers (mental health professionals) and cross-cultural patients is even more complicated with a third person i.e. interpreter, involved. However, refugees and asylum seekers still receive the most of treatment provided via interpreters. Innovative solution for this problem might be “cross-cultural telepsychiatry model” within various settings. Since 2004, “cross-cultural telepsychiatry” has been tested, developed and established in outskirts areas of of Denmark through various pilot projects. Overall high patient satisfaction was reported by patients as well as by involved professionals.

Sleep spindles are transient waveforms and one of the key features that contributes to sleep stages assessment. Due to the large number of sleep spindles appearing on an overnight sleep, automating the detection of this waveforms is desirable. This paper presents a comparative study over the sleep spindle classification task involving the discrete wavelet decomposition of the EEG signal, and seven different classification algorithms. The main goal was to find a classifier that achieves the best performance. The results reported that Random Forest stands out over the rest of models, achieving an accuracy value of $$94.08 \pm 2.8$$94.08±2.8 and $$94.08 \pm 2.4\,\%$$94.08±2.4% with the symlet and biorthogonal wavelet families.

Head-mounted displays are increasingly available and have been proposed as a platform for a new class of low vision aid. Two kinds of head-mounted display, smart glasses and a smartphone-based headset, are evaluated here in terms of their visibility and desirability for users with age-related macular degeneration. This evaluation is performed through a test to measure the extent of visibility of the screens, through a reading speed test and through a questionnaire. All but one of the participants could read from the displays and see at least 45 % of at least one of the screens. The majority found it easier to read from the displays than from paper.

In this work we present a novel non-invasive method and the corresponding devices to diagnose ansinternal anomality, that is, various kinds of intrinsic cancer, in a living subject by sending a passively occurring middle-infrared (MIR) radiation signal associated with the abnormality and inside an orifice of the Diagnostics includes detection and identification of the abnormality. A device or instrument is used either to bring a sensor into the orifice (in vivo diagnosis) or to transmit the MIR signal to the device or instrument located outside of the orifice (in vitro diagnosis). The example of instrument includes a prior art endoscope or gastroscope. The corresponding test results are presented as a proof of the proposed methodology of earlier diagnostics of internal cancerous structures.

The proposed methodology is focus as an alternative to analyze and describe the most accurate social phenomena according our reality using different computational mathematical theories, which are not used conventionally in social sciences applications and this is a new approach to create new computer’s simulation architectures.

It is essential to provide better living conditions for vulnerable sectors of society using technology and it is important to consider that the technology must be friendly with users, and even adapt to their needs and desires. We can see in current systems the user has to learn to use the devices or services, but with an intelligence system, the technology is a very effective way to determine the needs of users. In this research, we present the physical implementation of a system to assist users and patients in daily activities or duties. The system include a architecture of agents where a Deliberative agent learns from the interaction with the user, in this way the system detects thermal comfort preferences for give an automatic assistance. We propose an algorithm with a proactive stage and learning stage adapting a classification algorithm. We select the classification algorithm with the best performance using cross validation. The algorithms of pattern recognition was Back Propagation neural network, Naïve Bayes, Minimum Distance and KNN (k near neighbor). Our motivation of this work was to help people with motor difficulties or people who use wheelchairs, for this reason it was essential to use a wireless controller and use a friendly interface. The system was implemented in a testbed at the Leon Institute of Technology in Guanajuato, Mexico, and include sensors of humidity and temperature, windows actuators, wireless agents and other devices. Experimental tests were performed with data collected during a time period and using use cases. The results were satisfactory because it was not only possible remote assistance by the user but it was possible to obtain user information to learn comfort preferences using vector features proposed and selecting the classification algorithm with better performance.

The evolution of technology allows to people with special capabilities of mobility to perform the activities faster and easier. The intelligent environments combined with optimization algorithms and middleware agents could help to this aim. This paper presents the design and the implementation of an architecture of a middleware agent that allows us to make the communication between heterogeneous devices (sensors and actuators of different communication protocols from WiFi to ZigBee). On the other hand, we present a comparison study between micro-algorithms used to get lighting comfort in order to perform an activity in a confined space; this is affect by the light from the outside, which can be blocked by shutters and doors, and lighting of lamps obtained within this space. The micro-algorithm evaluated were: Genetic Algorithm (GA), Artificial Immune System (AIS), Estimation Distribution Algorithm (EDA), Particle Swarm Optimization (PSO), Bee Algorithm (BA) and Bee Swarm Optimization (BSO).

Autism is a complex neurological disorder that typically lasts a lifetime. It is part of a group of disorders known as autism spectrum disorders. PPPs for Autism EdNinja, were developed by experienced therapists in the neurological disorder autism. This tool support in the treatment and development of skills a child with this disorder between the ages of 4–10 years. The company MindHUB developer of EdNinja APS is a company in the city of Tijuana, Baja California, Mexico; They APS these are developed considering the cultural characteristics of Mexico, not rear confusion in the child. To market to other countries this tool, you must do the translation and adaptation to the corresponding culture.

Health is an issue of significant importance to any country, in Mexico, government institutions actions promoting advancement plans and implementation of strategies that allow the inclusion of information and communications technology in supporting been made diagnosis, treatments and procedures in public health. The computerization of the health sector ensures efficient processes and access to accurate, timely information at the right time, elementary factors in decision-making. The use of media such as cloud computing, storage and efficient access to patient information. What is the real situation observed in the city of Tijuana, Baja California. This study presents the opinion of health experts in the public sector, on the use and interest to use technological tools as support in substantial activities in the provision of health care.

Understanding the mechanisms of invasion of cancer cells into surrounding tissues is of primary importance for limiting tumor progression. The degradation of the extracellular matrix (ECM) and the consequent invasion of the surrounding tissue by tumor cells represent the first stage in the development and dissemination of metastasis. The quantification of such a degradation is thus an important parameter to evaluate the metastatic potential of tumor cells. Assessment of degradation is usually performed in in vitro assays, in which tumor cells are cultured on a gelatin (or other matrix)-coated dishes and the degraded gelatin areas under the tumor cells are visualized and quantified by fluorescent labelling. In this paper, we present an automatic method to quantify the ECM degradation through the feature analysis of the digital images, obtained from the in vitro assays and showing the tumor cells and the degraded gelatin areas. Differently from the existing methods of image analysis supporting biologists, our method does not require any interaction with the user providing quickly corrected and unbiased measures. Comparative results with a method frequently used by biologists, has been performed.

Post-stroke rehabilitation has an enormous impact on health services worldwide because of the high prevalence of stroke, in continuous growth due to the progressive population aging. Systems for neuro-motor rehabilitation at home can help reduce the economic burden of long lasting treatment in chronic post-stroke patients; however the efficacy of these systems in providing a correct and effective rehabilitation should be established. From this point of view, coupling home rehabilitation systems with quantitative EEG methodologies for objectively characterizing patients’ cerebral activity could be useful for the clinician to optimize the rehabilitation protocol and assess its efficacy. Moreover, the use of virtual/augmented reality technologies could assist the patients during unsupervised rehabilitation by providing an empathic feedback to improve their adherence to the treatment. These two aspects were studied and implemented in RIPRENDO@home, a multidisciplinary project, aimed to develop an integrated technological platform oriented to home neurorehabilitation for stroke patients.

This paper describes a novel web-based environment that empowers healthcare research communities to efficiently and effectively collaborate thanks to reliable and user-friendly access to integrated and interoperable resources of different types. The proposed solution enables heterogeneous data and knowledge sources, as well as healthcare-related processing methodologies and tools, to be wrapped through appropriate web services. The paper sketches the motivation behind the proposed solution, discusses its architecture and types of services to be integrated, and comments on the foreseen advancements in healthcare research from a technological, a collaborative and an organizational perspective.

Nowadays, mHealth applications have been evolving in the form of pervasive solutions for supporting healthy life-style and wellness self-management. In such a direction, the Italian project “Smart Health 2.0” realized innovative technological infrastructures, on which different mHealth applications and services were developed, aimed at remotely supporting individuals in diseases prevention and improving their welfare and life styles. In this paper, the ontology-based approach proposed in the project to represent, share, and reason on the knowledge characterizing a subject within mHealth applications is presented. The proposed approach uses a hybrid strategy integrating ontology models and deductive rules built on the top of them. In order to better describe the proposed approach, a case of application has been presented with respect to an mHealth application designed for managing diet according to given daily caloric needs.

In the context of the extraction of the semantic contents important for the effective exploitation of the documents which are now made available by medical information systems, we consider the processing of relations connecting named entities and propose an unsupervised approach to their recognition and labeling. The approach is applied to an Italian data set of medical reports, and interesting results are presented and discussed from a qualitative point of view.

In order to protect personal information, numerous works has been done in watermarking field. However, there still leaves some problems to be solved: (1) most of the watermarking methods were processed in the plaintext domains, which leave latent risk of exposing host image information, thus it is needed to encrypt the host image and process the watermarking scheme in the encrypted domain; (2) numerous image encryption methods had been searched, while not all of them can meet the robustness requirements when applied in the encrypted domain; (3) for some special fields of watermarking applications, medical images, for example, image integrity is an important criterion that should be strictly taken into account. Thus, that kind of watermarking methods which applies by modifying the pixel values are not suitable in this situation. In order to achieve information hiding in such kind of images, special techniques which do not change image integrity is needed. (4) By utilizing homomorphic encryption scheme, one can process watermark extraction without decrypting the encrypted watermarked image first, while it cost too much time in image encryption and decryption, the computational speed need to be improved. Based on the points mentioned above, we proposed a robust zero-watermarking scheme in the DWT-DFT encrypted domain, which embeds and extracts watermark without modifying the pixel values. Firstly, we encrypted both original medical image and watermark image. Then, we extract the DWT-DFT low frequency coefficients as encrypted medical images’ feature vector. In watermark embedding and extraction phases, we adopt zero-watermarking technique to ensure integrity of medical images. Taking “db2” wavelet transform for example, we conduct the experiments on the visual quality and robustness of our watermarking scheme. Experimental results demonstrate that our algorithm achieves not only good watermarking robustness, but also ideal computation speed in the homomorphic encrypted domain.

The purpose of this study is to construct a system for surgical assistance by touchless interactions. In the clinical site, surgeons usually need to use some contacting devices to display or visualize medical images and check the anatomic information of the patient. However, after operating the visualization device, re-sterilization is necessary in order to maintain hygiene. Though some touchless surgery support systems using Kinect have been developed, their functions are not enough for surgical support. In this paper, we develop a new system, which can visualize 3D medical image by simple touchless single-handed interactions.

Super-resolution techniques have been widely used in fields such as television, aerospace imaging, and medical imaging. In medical imaging, X-rays commonly have low resolution and a significant amount of noise, because radiation levels are minimized to maintain patient safety. In this paper, we propose a novel super-resolution method for X-ray images, and a novel measurement algorithm for treatment of rheumatoid arthritis (RA) using X-ray images generated by our proposed super-resolution method. Moreover, to validate measurement accuracy for our proposed algorithm, we make a model for measurement algorithm about joint space distance using a 3D printer, and X-ray images are obtained to photograph it. Experimental results show that high quality super-resolution images are obtained, and the measurement distances are measured with high accuracy. Therefore, our proposed measurement algorithm is effective for RA medical examinations.

Automatic liver lesion enhancement and detection has an essential role for the computer-aided diagnosis of liver tumor in CT volume data. This paper proposes a novel lesion enhancement strategy using Bayesian framework by combining the lesion probabilities based on an adaptive non-parametric model with the processed test volume and the constructed common non-lesion models with prepared liver database. Due to the large variation of different lesion tissues, it is difficult to obtain the common lesion prototypes from liver volumes, and thus this paper investigates a lesion-training-data free strategy by only constructing the healthy liver and vessel prototypes using local patches, which can be extracted from any slice of the test liver volume, and is also easy to prepare the common training non-lesion samples for all volumes. With the healthy liver and vessel prototypes from the test volume, an adaptive non-parametric model is constructed for estimating the lesion possibility, which is considered as the pixel likelihood to lesion region; the common model constructed using the pre-prepared liver database is used to estimate the pixel probability, which is defined as prior knowledge due to the used unvaried model. Finally, the posterior probabilities based on Bayesian theory are achieved for enhancing lesion regions. Experimental results validate that the proposed framework can not only detect almost small lesion regions but also greatly reduce falsely detect regions.

In medical, scientific, and other fields, transparent surface visualization is useful for investigating inner three-dimensional (3D) structures. Such visualization typically involves the use of polygon graphics in which the polygons must be sorted along the line of sight. However, sorting involves considerable computation time for large-scale data. Furthermore, the order of polygons in the sorting can often become indefinite, especially for intersecting surfaces. Therefore, particle-based volume rendering that does not require sorting is proposed as a transparent-rendering method. The proposed method obtains slice images with non-uniform opacity using color and opacity maps similar to volume rendering. The method initially generates the particles, a process it performs only once. It additionally employs particle shuffling, which requires considerably less computation time than particle sorting. To demonstrate the efficacy of the proposed method, we rendered 3D-fused images, including slice–slice and volume–slice images, for medical volumetric data. The results show that the performance of the proposed method is satisfactory in cases in which the area of the particle is greater than that of the cell.

Parkinsonian Syndrome (PS) or Parkinsonism is the second most common neurodegenerative disorder in the elderly. Currently there is no cure for PS, and it has important socio-economic implications due to the fact that PS progressively disables people in their ordinary daily tasks. However, precise and early diagnosis can definitely help to start the treatment in the early stages of the disease, improving the patient’s quality of life. The study of neurodegenerative diseases has been usually addressed by visual inspection and semi-quantitative analysis of medical imaging, which results in subjective outcomes. However, recent advances in statistical signal processing and machine learning techniques provide a new way to explore medical images yielding to an objective analysis, dealing with the Computer Aided Diagnosis (CAD) paradigm. In this work, we propose a method that selects the most discriminative regions on 123I-FP-CIT SPECT (DaTSCAN) images and learns features using deep-learning techniques. The proposed system has been tested using images from the Parkinson Progression Markers Initiative (PPMI), obtaining accuracy values up to 95 %, showing its robustness for PS pattern detection and outperforming the baseline Voxels-as-Features (VAF) approach, used as an approximation of the visual analysis.

The paper presents the research work done for development of a lightweight and low-cost robotic glove that post-stroke patients can use to recover hand functionality. The work focused on two directions for the robotic glove structure (exoskeleton and wearable soft robotic glove) and on two types of recuperative actions (tele-operation and program based actions). Given the performance tests ran for the robotic gloves, better results were shown with the wearable soft robotic glove that could also be combined with Functional Electrical Stimulation in order to improve the post-stroke hand rehabilitation.

Behavior and lifestyle are the key determinants of health, disease, disability and premature mortality. There is important evidence that demonstrates that unhealthy behaviors increase the risk of the onset of many diseases and therefore could be considered among the causes of the disease itself. The ambition of this app is to provide people with something similar to a personal trainer, an application that, after collecting a range of information on the individual, is able to classify her/him based on her/his individual characteristics (physical parameters and lifestyle) and then to propose specific recommendations to improve her/his well-being. By monitoring the evolution over time of these individual characteristics, the application can also give feedback on the effectiveness of the measures and therefore provide positive stimuli to motivate the user to continue the path taken.

“White-coat” effect designs those hypertensive subjects with “uncontrolled” office Blood Pressure (BP) but normal BP values when assessed by Ambulatory BP Monitoring (ABPM) or home BP monitoring (HBPM). Cardiovascular Risk (CV) risk is lower than those with real uncontrolled BP but it still remains unclear if it is comparable to those well controlled hypertensive subjects. This paper presents the study, the results and the web platform that was designed and implemented which make possible the study of the “White-coat” effect as a subclinical target organ damage. The large amount of information that needs to be gathered, calculated and analyzed makes specially complicated, even almost inviable, the development of the study by traditional manual routine. This motivated the implementation of a platform that permitted the doctors make the study with guarantees. The implemented web platform organizes the information of the patients, presenting all the necessary information for the study, including physical and clinical parameters or 48-h ABPM processing. It also automatically estimates glomerular filtration rate by MDRD equation (GFR) and Sokolow-Lyon criteria for left ventricular hypertrophy (LVH) as well as different statistics from the 48-h ABPM. The platform facilitates the doctor’s work avoiding large and tedious manual processes, minimizing the risk of possible miscalculations and analyzing all the information in a easier way. This framework helped the doctors to recognize the so called “ABPM effect”, and what is more important in the management of hypertensive subjects, it helps to better identify hypertensive subjects at poor cardiovascular prognosis.

With the advent of the technology, the DNA sequencing has become cheaper and faster. Next-Generation Sequencing platforms are providing new opportunities to address biological and medical issues. However, they present new challenges of storing, handling and processing, as they produce massive amounts of data. Powerful computational infrastructure, new bioinformatics softwares and skilled people in programming are required to work with the analysis tools. This project aims to design and develop an intelligent system that analyses high-throughput datasets, with the purpose of improving the effectiveness in the biological and medical research fields. The target is to make a user-friendly tool that allows the user to automatically or manually design the desired analysis workflow. Therefore, the technological challenges consist in: (i) an interface between clinician and bioinformatics language, (ii) an intelligent tool that selects the appropriate analysis workflow and (iii) a solution that can handle, store and manage big datasets at a reasonable-price. In order to tackle these bottlenecks, a cloud-based prototype enhanced by a graphical user-friendly interface and implemented using Amazon Web Service.

White matter (WM) lesions are a phenomena perceived in magnetic resonance imaging (MRI) which is prevalent in many different brain pathologies, hence the general interest in automated methods for lesion segmentation (LS). We provide a short review of some commonly used state-of-the-art approaches. The article is focused on the machine learning techniques which researches use to construct semi- and fully-automated tools for LS. In addition, we mention the preprocessing steps, features extraction, LS databases and validation techniques.

During the last years a high effort on standardization of Electronic Health Records has been made. Standards ISO EN 13606 and OpenEHR with their dual approach have promoted semantic interoperability in the real clinical practice. Recently, the focus has been set on the extraction of knowledge from the clinical information stored in EHR, but current approaches based on archetypes do not provide a complete solution regarding content structuration limitation. In this paper we propose an ontology for Hygehos Electronic Health Records (EHR), that we call the Hygehos Ontology. The introduction of such ontology on the EHR system will facilitate the development of reasoning and knowledge extraction tools over the stored clinical information. In our approach, we first align the Hygehos EHR to the dual model of OpenEHR and generate the corresponding archetypes for every part of the system. Secondly, we formalize a methodology for structuring the clinical contents of Hygehos EHR into the Hygehos Ontology.

The Electronic Health Record (EHR) is the central information object for healthcare and medical related industries. However, it has been given little academic attention per se, because it is always embedded in the information system of the hospitals. This paper consider several aspects of EHR management systems: security and privacy, data mining, design of decision support systems, acceptance by users and producers of health resources, and system implementation.

Due to limitations in conventional medical imaging and the restrictions imposed by both the anatomy and the surgical approach in pancreatic cancer, there is a need for methods to support intraoperative imaging in order to improve their accurate anatomical localization and the characterization of their nature. Laparoscopic ultrasounds (LUS) images and endoscopic videos can be used to extract useful information during the surgical procedures. A fast approach for acquiring an estimation of the tumor positioning and size through laparoscopic ultrasounds images has been developed. Based on the surgical video, endoscope 3D tracking is achieved by means of a Shape-from-Motion technique. Intraoperative imaging algorithms’ validation has been carried out in an ex vivo porcine model and results have shown the viability of exploiting them for structures characterization and their 3D reconstruction.