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Über dieses Buch

This book contains the best papers of the Second International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2009), organized by the Institute for Systems and Technologies of Information Control and Communi- tion (INSTICC), technically co-sponsored by the IEEE Engineering in Medicine and Biology Society (EMB), IEEE Circuits and Systems Society (CAS) and the Workflow Management Coalition (WfMC), in cooperation with AAAI and ACM SIGART. The purpose of the International Joint Conference on Biomedical Engineering S- tems and Technologies is to bring together researchers and practitioners, including engineers, biologists, health professionals and informatics/computer scientists, int- ested in both theoretical advances and applications of information systems, artificial intelligence, signal processing, electronics and other engineering tools in knowledge areas related to biology and medicine. BIOSTEC is composed of three co-located conferences; each specializes in one of the aforementioned main knowledge areas, namely: • BIODEVICES (International Conference on Biomedical Electronics and - vices) focuses on aspects related to electronics and mechanical engineering, - pecially equipment and materials inspired from biological systems and/or - dressing biological requirements. Monitoring devices, instrumentation sensors and systems, biorobotics, micro-nanotechnologies and biomaterials are some of the technologies addressed at this conference.



Invited Papers


Computational Intelligence and Image Processing Methods for Applications in Skin Cancer Diagnosis

Digital photography provides new powerful diagnostic tools in dermatology. Dermoscopy is a special photography technique which enables taking photos of skin lesions in chosen lighting conditions. Digital photography allows for seeing details of the skin changes under various enlargements and coloring. Computer-assisted techniques and image processing methods can be further used for image enhancement and analysis and for feature extraction and pattern recognition in the selected images. Special techniques used in skin-image processing are discussed in detail. Feature extraction methods and automated classification techniques based on statistical learning and model ensembling techniques provide very powerful tools which can assist the doctors in taking decisions. Performance of classifiers will be discussed in specific case of melanoma cancer diagnosis. The techniques have been tested on a large data set of images.

Maciej Ogorzałek, Grzegorz Surówka, Leszek Nowak, Christian Merkwirth

Affective Man-Machine Interface: Unveiling Human Emotions through Biosignals

As is known for centuries, humans exhibit an electrical profile. This profile is altered through various psychological and physiological proce-sses, which can be measured through biosignals; e.g., electromyography (EMG) and electrodermal activity (EDA). These biosignals can reveal our emotions and, as such, can serve as an advanced man-machine interface (MMI) for empathic consumer products. However, such a MMI requires the correct classification of biosignals to emotion classes. This chapter starts with an introduction on biosignals for emotion detection. Next, a state-of-the-art review is presented on automatic emotion classification. Moreover, guidelines are presented for affective MMI. Subsequently, a research is presented that explores the use of EDA and three facial EMG signals to determine neutral, positive, negative, and mixed emotions, using recordings of 21 people. A range of techniques is tested, which resulted in a generic framework for automated emotion classification with up to 61.31% correct classification of the four emotion classes, without the need of personal profiles. Among various other directives for future research, the results emphasize the need for parallel processing of multiple biosignals.

Egon L. van den Broek, Viliam Lisý, Joris H. Janssen, Joyce H. D. M. Westerink, Marleen H. Schut, Kees Tuinenbreijer



On-Chip Biosensors Based on Microwave Detection for Cell Scale Investigations

This paper presents original label free bio sensors allowing the study of electrical properties of human cells and so potentially cell identification and discrimination. Proposed biosensors are based on planar devices operating at microwave frequencies and fabricated using a standard microelectronic process. Actually, their microscopic sensitive areas allow an improved detection at the cell scale which represents a significant progress in the study of many biological phenomenon. In this paper, biosensor detection capabilities are demonstrated on only few biological cells analysis up to one single cell interacting with the sensor. Fabricated micro-sensors can be used to determine cell intrinsic electrical impedance at microwave frequencies allowing a label free approach to accurately discriminate biological cells.

Claire Dalmay, Arnaud Pothier, M. Cheray, Fabrice Lalloué, Marie-Odile Jauberteau, Pierre Blondy

Improvements of a Brain-Computer Interface Applied to a Robotic Wheelchair

Two distinct signal features suitable to be used as input to a Support-Vector Machine (SVM) classifier in an application involving hands motor imagery and the correspondent EEG signal are evaluated in this paper. Such features are the Power Spectral Density (PSD) components and the Adaptive Autoregressive (AAR) parameters. The best result (an accuracy of 97.1%) is obtained when using PSD components, while the AAR parameters generated an accuracy of 91.4%. The results also demonstrate that it is possible to use only two EEG channels (bipolar configuration around






), discarding the bipolar configuration around



. The algorithms were tested with a proprietary EEG data set involving 4 individuals and with a data set provided by the University of Graz (Austria) as well. The resulting classification system is now being implemented in a Brain-Computer Interface (BCI) used to guide a robotic wheelchair.

André Ferreira, Teodiano Freire Bastos-Filho, Mário Sarcinelli-Filho, José Luis Martín Sánchez, Juan Carlos García García, Manuel Mazo Quintas

Wavelet-Based and Morphological Analysis of the Global Flash Multifocal ERG for Open Angle Glaucoma Characterization

This article presents one of the alternative methods developed for the early detection of ocular glaucoma based on the characterisation of mfERG (multifocal electroretinography) readings. The digital signal processing technique is based on Wavelets, hitherto unused in this field, for detection of advanced-stage glaucoma and the study of signal morphology by means of identity patterns for detection of glaucoma in earlier stages. Future research possibilities are also mentioned, such as the study of orientation in the development of the disease.

J. M. Miguel-Jiménez, S. Ortega, I. Artacho, L. Boquete, J. M. Rodríguez-Ascariz, P. De La Villa, R. Blanco

Biotin-Streptavidin Sensitive BioFETs and Their Properties

In this work the properties of a biotin-streptavidin BioFET have been studied numerically with homogenized boundary interface conditions as the link between the oxide of the FET and the analyte which contains the bio-sample. The biotin-streptavidin reaction pair is used in purification and detection of various biomolecules; the strong streptavidin-biotin bond can also be used to attach biomolecules to one another or onto a solid support. Thus this reaction pair in combination with a FET as the transducer is a powerful setup enabling the detection of a wide variety of molecules with many advantages that stem from the FET, like no labeling, no need of expensive read-out devices, the possibility to put the signal amplification and analysis on the same chip, and outdoor usage without the necessity of a lab.

Thomas Windbacher, Viktor Sverdlov, Siegfried Selberherr

Improving Patient Safety with X-Ray and Anesthesia Machine Ventilator Synchronization: A Medical Device Interoperability Case Study

When a x-ray image is needed during surgery, clinicians may stop the anesthesia machine ventilator while the exposure is made. If the ventilator is not restarted promptly, the patient may experience severe complications. This paper explores the interconnection of a ventilator and simulated x-ray into a prototype plug-and-play medical device system. This work assists ongoing interoperability framework development standards efforts to develop functional and non-functional requirements and illustrates the potential patient safety benefits of interoperable medical device systems by implementing a solution to a clinical use case requiring interoperability.

David Arney, Julian M. Goldman, Susan F. Whitehead, Insup Lee

A Ceramic Microfluidic Device for Monitoring Complex Biochemical Reactive Systems

A 3-dimensional mesofluidic biological monitoring module has been successfully designed and fabricated using a low-temperature co-fired ceramic (LTCC) technology. This mesofluidic device consists of a network of micro-channels, a spherical mixing cavity and measuring ports. A selection of appropriate commercially available ceramic tapes has been chosen with regard to their biocompatibility performance. Specific processing procedures required for the realization of such a complex structure are demonstrated. Three dimensional numerical flow simulations have been conducted to characterize the concentration profiles of liquids at a specific measuring port and verified by experiment.

Walter Smetana, Bruno Balluch, Ibrahim Atassi, Philipp Kügler, Erwin Gaubitzer, Michael Edetsberger, Gottfried Köhler

Knee Angle Estimation Algorithm for Myoelectric Control of Active Transfemoral Prostheses

This paper presents a bioinstrumentation system for the acquisition and pre-processing of surface electromyographic (SEMG) signals, and a knee angle estimation algorithm for control of active transfemoral leg prostheses, using methods for feature extraction and classification of myoelectric signal patterns. The presented microcontrolled bioinstrumentation system is capable of recording up to four SEMG channels, and one electrogoniometer channel. The proposed neural myoelectric controller algorithm is capable of predicting the intended knee joint angle from the measured SEMG signals. The algorithm is designed in three stages: feature extraction, using auto-regressive model and amplitude histogram; feature projection, using self organizing maps; and pattern classification, using a Levenberg-Marquardt neural network. The use of SEMG signals and additional mechanical information such as that provided by the electrogoniometer may improve precision in the control of leg prostheses. Preliminary results are presented.

Alberto López Delis, João Luiz Azevedo de Carvalho, Adson Ferreira da Rocha, Francisco Assis de Oliveira Nascimento, Geovany Araújo Borges

Micro Droplet Transfer between Superhydrophobic Surfaces via a High Adhesive Superhydrophobic Surface

Micro droplet handling is very important for micro and nano fluidic devices and an intelligent bio interface. Micro droplet transfer via a high adhesive superhydrophobic surface has been reported in recent years. We demonstrated water droplet adhesion controllable superhydrophobic metal-polymer surfaces. Moreover we achieved micro droplet transfer between superhydrophobic surfaces by using different droplet adhesion properties. Water micro droplets were transferred from a low-adhesive superhydrophobic surface to a midium-adhesive superhydrophobic surface via a high-adhesive superhydrophobic surface without any mass loss. After droplet transfer, water contact angle was about 150°. Droplet handlings on the adhesive superhydrophobic surfaces will be expected for fluidic bio devices with energy saving.

Daisuke Ishii, Hiroshi Yabu, Masatusgu Shimomura



Study on Biodegradation Process of Polyethylene Glycol with Exponential Glowth of Microbial Population

Biodegradation of polyethylene glycol is studied mathematically. A mathematical model for depolymerization process of exogenous type is described. When a degradation rate is a product of a time factor and a molecular factor, a time dependent model can be transformed into a time independent model, and techniques developed in previous studies can be applied to the time independent model to determine the molecular factor. The time factor can be determined assuming the exponential growth of the microbial population. Those techniques are described, and numerical results are presented. A comparison between a numerical result and an experimental result shows that the mathematical method is appropriate for practical applications.

Masaji Watanabe, Fusako Kawai

Variable Down-Selection for Brain-Computer Interfaces

A new formulation of principal component analysis (PCA) that considers group structure in the data is proposed as a variable down-selection method. Optimization of electrode channels is a key problem in brain-computer interfaces (BCI). BCI experiments generate large feature spaces compared to the sample size due to time limitations in EEG sessions. It is essential to understand the importance of the features in terms of physical electrode channels in order to design a high performance yet realistic BCI. The proposed algorithm produces a ranked list of original variables (electrode channels or features), according to their ability to discriminate movement imagery tasks. A linear discrimination analysis (LDA) classifier is applied to the selected variable subset. Evaluation of the down-selection method using synthetic datasets selected more than 83% of relevant variables. Classification of imagery tasks using real BCI datasets resulted in less than 19% classification error. Across-Group Variance (AGV) showed the best classification performance with the largest dimensionality reduction in comparison with other algorithms in common use.

Nuno S. Dias, Mst Kamrunnahar, Paulo M. Mendes, Steven J. Schiff, Jose H. Correia

Effect of a Simulated Analogue Telephone Channel on the Performance of a Remote Automatic System for the Detection of Pathologies in Voice: Impact of Linear Distortions on Cepstrum-Based Assessment - Band Limitation, Frequency Response and Additive Noise

Advances in speech signal analysis during the last decade have allowed the development of automatic algorithms for a non-invasive detection of laryngeal pathologies. Performance assessment of such techniques reveals that classification success rates over 90 % are achievable. Bearing in mind the extension of these automatic methods to remote diagnosis scenarios, this paper analyses the performance of a pathology detector based on Mel Frequency Cepstral Coefficients when the speech signal has undergone the distortion of an analogue communications channel, namely the phone channel. Such channel is modeled as a concatenation of linear effects. It is shown that while the overall performance of the system is degraded, success rates in the range of 80% can still be achieved. This study also shows that the performance degradation is mainly due to band limitation and noise addition.

Rubén Fraile, Nicolás Sáenz-Lechón, Juan Ignacio Godino-Llorente, Víctor Osma-Ruiz, Corinne Fredouille

A Biologically-Inspired Visual Saliency Model to Test Different Strategies of Saccade Programming

Saliency models provide a saliency map that is a topographically arranged map to represent the saliency of the visual scene. Saliency map is used to sequentially select particular locations of the scene to predict a subject’s eye scanpath when viewing the corresponding scene. A saliency map is most of the time computed using the same point of view or foveated point. Few models were interested in saccade programming strategies. In visual search tasks, studies shown that people can plan from one foveated point the next two saccades (and so, the next two fixations): this is called concurrent saccade programming. In this paper, we tested if such strategy occurs during natural scene free viewing. We tested different saccade programming strategies depending on the number of programmed saccades. The results showed that the strategy of programming one saccade at a time from the foveated point best matches the experimental data from free viewing of natural images. Because saccade programming models depend on the foveated point, we took into account the spatially variant retinal resolution. We showed that the predicted eye fixations were more effective when this retinal resolution was combined with the saccade programming strategies.

Tien Ho-Phuoc, Anne Guérin-Dugué, Nathalie Guyader

Transition Detection for Brain Computer Interface Classification

This paper deals with the classification of signals for brain-computer interfaces (BCI). We take advantage of the fact that thoughts last for a period, and therefore EEG samples run in sequences belonging to the same class (thought). Thus, the classification problem can be reformulated into two subproblems: detecting class transitions and determining the class for sequences of samples between transitions. The method detects transitions when the L1 norm between the power spectra at two different times is larger than a threshold. To tackle the second problem, samples are classified by taking into account a window of previous predictions. Two types of windows have been tested: a constant-size moving window and a variable-size growing window. In both cases, results are competitive with those obtained in the BCI III competition.

Ricardo Aler, Inés M. Galván, José M. Valls

Tuning Iris Recognition for Noisy Images

The use of iris recognition for human authentication has been spreading in the past years. Daugman has proposed a method for iris recognition, composed by four stages: segmentation, normalization, feature extraction, and matching. In this paper we propose some modifications and extensions to Daugman’s method to cope with noisy images. These modifications are proposed after a study of images of CASIA and UBIRIS databases. The major modification is on the computationally demanding segmentation stage, for which we propose a faster and equally accurate template matching approach. The extensions on the algorithm address the important issue of pre-processing, that depends on the image database, being mandatory when we have a non infra-red camera, like a typical WebCam. For this scenario, we propose methods for reflection removal and pupil enhancement and isolation. The tests, carried out by our C# application on grayscale CASIA and UBIRIS images, show that the template matching segmentation method is more accurate and faster than the previous one, for noisy images. The proposed algorithms are found to be efficient and necessary when we deal with non infra-red images and non uniform illumination.

Artur Ferreira, André Lourenço, Bárbara Pinto, Jorge Tendeiro

Three-Dimensional Reconstruction of Macroscopic Features in Biological Materials

This paper covers the topic of three dimensional reconstruction of small textureless formations usually found in biological samples. Generally used reconstructing algorithms do not provide sufficient accuracy for surface analysis. In order to achieve better results, combined strategy was developed, linking stereo matching algorithms with monocular depth cues such as depth from focus and depth from illumination.

Proposed approach is practically tested on bryophyte canopy structure. Recent studies concerning bryophyte structure applied various modern, computer analysis methods for determining moss layer characteristics drawing on the outcomes of a previous research on surface of soil. In contrast to active methods, this method is a non-contact passive, therefore, it does not emit any kind of radiation which can lead to interference with moss photosynthetic pigments, nor does it affect the structure of its layer. This makes it much more suitable for usage in natural environment.

Michal Krumnikl, Eduard Sojka, Jan Gaura, Oldřich Motyka

Wavelet Transform Analysis of the Power Spectrum of Centre of Pressure Signals to Detect the Critical Point Interval of Postural Control

The aim of this study was to develop a method to detecting the critical point interval (CPI) when sensory feedback is used as part of a closed-loop postural control strategy. Postural balance was evaluated using centre of pressure (COP) displacements from a force plate for 17 control and 10 elderly subjects under eyes open, eyes closed, and vibration conditions. A modified local-maximum-modulus wavelet transform analysis using the power spectrum of COP signals was used to calculate CPI. Lower CPI values indicate increased closed-loop postural control with a quicker response to sensory input. Such a strategy requires greater energy expenditure due to the repeated muscular interventions to remain stable. The CPI for elderly occurred significantly quicker than for controls, indicating tighter control of posture. Similar results were observed for eyes closed and vibration conditions. The CPI parameter can be used to detect differences in postural control due to ageing.

Neeraj Kumar Singh, Hichem Snoussi, David Hewson, Jacques Duchêne

Early Detection of Severe Apnoea through Voice Analysis and Automatic Speaker Recognition Techniques

This study is part of an on-going collaborative effort between the medical and the signal processing communities to promote research on applying voice analysis and Automatic Speaker Recognition techniques (ASR) for the automatic diagnosis of patients with severe obstructive sleep apnoea (OSA). Early detection of severe apnoea cases is important so that patients can receive early treatment. Effective ASR-based diagnosis could dramatically cut medical testing time. Working with a carefully designed speech database of healthy and apnoea subjects, we present and discuss the possibilities of using generative Gaussian Mixture Models (GMMs), generally used in ASR systems, to model distinctive apnoea voice characteristics (i.e. abnormal nasalization). Finally, we present experimental findings regarding the discriminative power of speaker recognition techniques applied to severe apnoea detection. We have achieved an 81.25 % correct classification rate, which is very promising and underpins the interest in this line of inquiry.

Ruben Fernández, Jose Luis Blanco, David Díaz, Luis A. Hernández, Eduardo López, José Alcázar

Automatic Detection of Atrial Fibrillation for Mobile Devices

Two versions of a new detector for automatic real-time detection of atrial fibrillation in non-invasive ECG signals are introduced. The methods are based on beat to beat variability, tachogram analysis and simple signal filtering. The implementation on mobile devices is made possible due to the low demand on computing power of the employed analysis procedures. The proposed algorithms correctly identified 436 of 440 five minute episodes of atrial fibrillation or flutter and also correctly identified up to 302 of 342 episodes of no atrial fibrillation, including normal sinus rhythm as well as other cardiac arrhythmias. These numbers correspond to a sensitivity of 99.1 % and a specificity of 88.3%.

Stefanie Kaiser, Malte Kirst, Christophe Kunze

Speaker-Adaptive Speech Recognition Based on Surface Electromyography

We present our recent advances in

silent speech

interfaces using electromyographic signals that capture the movements of the human articulatory muscles at the skin surface for recognizing continuously spoken speech. Previous systems were limited to speaker- and session-dependent recognition tasks on small amounts of training and test data. In this article we present speaker-independent and speaker-adaptive training methods which allow us to use a large corpus of data from many speakers to train acoustic models more reliably. We use the speaker-dependent system as baseline, carefully tuning the data preprocessing and acoustic modeling. Then on our corpus we compare the performance of speaker-dependent and speaker-independent acoustic models and carry out model adaptation experiments.

Michael Wand, Tanja Schultz

Towards the Development of a Thyroid Ultrasound Biometric Scheme Based on Tissue Echo-morphological Features

This paper proposes a biometric system based on features extracted from the thyroid tissue accessed through 2D ultrasound. Tissue echo-morphology, which accounts for the intensity (echogenicity), texture and structure has started to be used as a relevant parameter in a clinical setting. In this paper, features related to texture, morphology and tissue reflectivity are extracted from the ultrasound images and the most discriminant ones are selected as an input for a prototype biometric identification system. Several classifiers were tested, with the best results being achieved by a combination of classifiers (k-Nearest Neighbors, MAP and entropy distance). Using leave-one-out cross-validation method the identification rate was up to 94%. Features related to texture and echogenicity were tested individually with high identification rates up to 78% and 70%, respectively. This suggests that the acoustic impedance (reflectivity or echogenicity) of the tissue as well as texture are feasible parameters to discriminate between distinct subjects. This paper shows the effectiveness of the proposed classification, which can be used not only as a new biometric modality but also as a diagnostic tool.

Josè C. R. Seabra, Ana L. N. Fred



Collecting, Analyzing, and Publishing Massive Data about the Hypertrophic Cardiomyopathy

We present in this paper the architecture and some implementation details of a Document Management System and Workflow to help in the diagnosis of the hypertrophic cardiomyopathy, one of the most frequent genetic cardiovascular diseases. The system allows a gradual and collaborative creation of a knowledge base about the mutations associated with this disease. The system manages both the original documents of the scientific papers and the data extracted from these papers by the experts. Furthermore, a semiautomatic report generation module exploits this knowledge base to create high quality reports about the studied mutations.

Lorenzo Montserrat, Jose Antonio Cotelo-Lema, Miguel R. Luaces, Diego Seco

BredeQuery: Coordinate-Based Meta-analytic Search of Neuroscientific Literature from the SPM Environment

Large amounts of neuroimaging studies are collected and have chan-ged our view on human brain function. By integrating multiple studies in meta-analysis a more complete picture is emerging. Brain locations are usually reported as coordinates with reference to a specific brain atlas, thus some of the databases offer so-called coordinate-based searching to the users (e.g. Brede, BrainMap). For such search, the publications, which relate to the brain locations represented by the user coordinates, are retrieved. We present BredeQuery – a plugin for the widely used SPM data analytic pipeline. BredeQuery offers a direct link from SPM to the Brede Database coordinate-based search engine. BredeQuery is able to ‘grab’ brain location coordinates from the SPM windows and enter them as a query for the Brede Database. Moreover, results of the query can be displayed in a MATLAB window and/or exported directly to some popular bibliographic file formats (BibTeX, Reference Manager, etc).

Bartłomiej Wilkowski, Marcin Szewczyk, Peter Mondrup Rasmussen, Lars Kai Hansen, Finn Årup Nielsen

Simulation of ECG Repolarization Phase with Improved Model of Cell Action Potentials

An improved model of action potentials (AP) is proposed to increase the accuracy of simulated electrocardiograms (ECGs). ECG simulator is based on a spatial model of a left ventricle, composed of cubic cells. Three distinct APs, modeled with functions proposed by Wohlfard, have been assigned to the cells, forming epicardial, mid, and endocardial layers. Identification of exact parameter values for AP models has been done through optimization of the simulated ECGs. Results have shown that only through an introduction of a minor extension to the AP model, simulator is able to produce more realistic ECGs. The same extension also proves essential for achieving a better fit between the measured and modeled APs.

Roman Trobec, Matjaž Depolli, Viktor Avbelj

Advances in Computer-Based Autoantibodies Analysis

Indirect Immunofluorescence (IIF) imaging is the recommended me-thod to detect autoantibodies in patient serum, whose common markers are antinuclear autoantibodies (ANA) and autoantibodies directed against double strand DNA (anti-dsDNA). Since the availability of accurately performed and correctly reported laboratory determinations is crucial for the clinicians, an evident medical demand is the development of Computer Aided Diagnosis (CAD) tools supporting physicians’ decisions.

In this paper we present a comprehensive system that helps in recognising the presence of ANA and anti-dsDNA autoantibodies. The analysis of CAD performance shows its potential in lowering the method variability, in increasing the level of standardization and in serving as a second reader reducing the physicians’ workload. The system has been successfully tested on annotated datasets.

Paolo Soda, Giulio Iannello

Support Vector Machine Diagnosis of Acute Abdominal Pain

This study explores the feasibility of a decision-support system for patients seeking care for acute abdominal pain, and, specifically the diagnosis of acute diverticulitis. We used a linear support vector machine (SVM) to separate diverticulitis from all other reported cases of abdominal pain and from the important differential diagnosis non-specific abdominal pain (NSAP). On a database containing 3337 patients, the SVM obtained results comparable to those of the doctors in separating diverticulitis or NSAP from the remaining diseases. The distinction between diverticulitis and NSAP was, however, substantially improved by the SVM. For this patient group, the doctors achieved a sensitivity of 0.714 and a specificity of 0.963. When adjusted to the physicians’ results, the SVM sensitivity/specificity was higher at 0.714/0.985 and 0.786/0.963 respectively. Age was found as the most important discriminative variable, closely followed by C-reactive protein level and lower left side pain.

Malin Björnsdotter, Kajsa Nalin, Lars-Erik Hansson, Helge Malmgren

Near Field Communication and Health: Turning a Mobile Phone into an Interactive Multipurpose Assistant in Healthcare Scenarios

In this paper we discuss the introduction of the Near Field Communication (NFC) technology in the management of the assistance operations in the hospitals. NFC is a new short range communication system based on RFID technology.

NFC systems can work like traditional RFID systems, where a master device reads some information from a slave device, but they can also set up a two-way communication between two items. In particular, NFC devices can be integrated on mobile phones, widely enhancing the intercommunication capabilities of the users.

The introduction of NFC in sanitary environments can help to make safer all the assistance operations. Next to the realization of a NFC electronic case history, we also studied the realization of electronic medical prescription and the use of this technology for the exchange of patient data between doctors and between nurses, in order to avoid errors in the attendance operations.

The final idea is to change a mobile phone into an interactive multipurpose assistant for people working in hospitals or in harness with patients.

Giuliano Benelli, Alessandro Pozzebon

Electronic Health Records: An Enhanced Security Paradigm to Preserve Patient’s Privacy

In recent years, demographic change and increasing treatment costs demand the adoption of more cost efficient, highly qualitative and integrated health care processes. The rapid growth and availability of the Internet facilitate the development of eHealth services and especially of electronic health records (EHRs) which are promising solutions to meet the aforementioned requirements. Considering actual web-based EHR systems, patient-centric and patient moderated approaches are widely deployed. Besides, there is an emerging market of so called personal health record platforms, e.g. Google Health. Both concepts provide a central and web-based access to highly sensitive medical data. Additionally, the fact that these systems may be hosted by not fully trustworthy providers necessitates to thoroughly consider privacy issues. In this paper we define security and privacy objectives that play an important role in context of web-based EHRs. Furthermore, we discuss deployed solutions as well as concepts proposed in the literature with respect to this objectives and point out several weaknesses. Finally, we introduce a system which overcomes the drawbacks of existing solutions by considering an holistic approach to preserve patient’s privacy and discuss the applied methods.

Daniel Slamanig, Christian Stingl

Augmented Feedback System to Support Physical Therapy of Non-specific Low Back Pain

Low back pain is an important problem in industrialized countries. Two key factors limit the effectiveness of physiotherapy: low compliance of patients with repetitive movement exercises, and inadequate awareness of patients of their own posture. The Backtrainer system addresses these problems by real-time monitoring of the spine position, by providing a framework for most common physiotherapy exercises for the low back, and by providing feedback to patients in a motivating way. A minimal sensor configuration was identified as two inertial sensors that measure the orientation of the lower back at two points with three degrees of freedom. The software was designed as a flexible platform to experiment with different hardware, and with various feedback modalities. Basic exercises for two types of movements are provided: mobilizing and stabilizing. We developed visual feedback - abstract as well as in the form of a virtual reality game - and complemented the on-screen graphics with an ambient feedback device. The system was evaluated during five weeks in a rehabilitation clinic with 26 patients and 15 physiotherapists. Subjective satisfaction of subjects was good, and we interpret the results as encouraging indication for the adoption of such a therapy support system by both patients and therapists.

Dominique Brodbeck, Markus Degen, Michael Stanimirov, Jan Kool, Mandy Scheermesser, Peter Oesch, Cornelia Neuhaus

Multi-analytical Approaches Informing the Risk of Sepsis

Sepsis is a significant cause of mortality and morbidity and is often associated with increased hospital resource utilization, prolonged intensive care unit (ICU) and hospital stay. The economic burden associated with sepsis is huge. With advances in medicine, there are now aggressive goal oriented treatments that can be used to help these patients. If we were able to predict which patients may be at risk for sepsis we could start treatment early and potentially reduce the risk of mortality and morbidity. Analytic methods currently used in clinical research to determine the risk of a patient developing sepsis may be further enhanced by using multi-modal analytic methods that together could be used to provide greater precision. Researchers commonly use univariate and multivariate regressions to develop predictive models. We hypothesized that such models could be enhanced by using multiple analytic methods that together could be used to provide greater insight. In this paper, we analyze data about patients with and without sepsis using a decision tree approach and a cluster analysis approach. A comparison with a regression approach shows strong similarity among variables identified, though not an exact match. We compare the variables identified by the different approaches and draw conclusions about the respective predictive capabilities,while considering their clinical significance.

Femida Gwadry-Sridhar, Benoit Lewden, Selam Mequanint, Michael Bauer


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