Abstract
In this paper, we consider the problem of separating a set of independent components when only one movable sensor is available to record the mixtures. We propose to exploit the quasi-periodicity of the heart signals to transform the signal from this one moving sensor, into a set of measurements, as if from a virtual array of sensors. We then use ICA to perform source separation. We show that this technique can be applied to heart sounds and to electrocardiograms.

Abstract
In this paper, we investigate the applicability of Electrocardiogram (ECG) signals for human identification. Wavelet Transform (WT) and Independent Component Analysis (ICA) methods are applied to extract morphological features that appear to offer excellent discrimination among subjects. The proposed method is aimed at the two-lead ECG configuration that is routinely used in long-term continuous monitoring of heart activity. The information from the two ECG leads is fused to achieve improved subject identification. The proposed method was tested on three public ECG databases, namely, MIT-BIH Arrhythmias Database [1], MIT-BIH Normal Sinus Rhythm Database [2] and Long-Term ST Database [3], in order to evaluate the proposed subject identification method on normal ECG signals as well as ECG signals with arrhythmias. Excellent rank-1 recognition rates (as high as 99.6%) were achieved based on single heartbeats. The proposed method exhibits good identification accuracies not just with the normal ECG signals, but also in the presence of various arrhythmias. This work adds to the growing evidence that ECG signals can be useful for human identification. © 2010 IEEE.

Abstract
The endoscopic capsule is a recent technological breakthrough with high clinical importance. Exam analysis duration is its main setback, requiring an average of two hours from a trained specialist. Automation is required and this paper presents a topographic segmentation tool using low-level features that can reduce annotation times up to 15 minutes per exam. This is accomplished using Bayesian classifiers and MPEG-7 visual descriptors.

Abstract
The goal of the Vital Responder research project is to explore the synergies between innovative wearable technologies, scattered sensor networks, intelligent building technology and precise localization services to provide secure, reliable and effective first-response systems in critical emergency scenarios. Critical events, such as natural disaster or other large-scale emergency, induce fatigue and stress in first responders, such as fire fighters, policemen and paramedics. There are distinct fatigue and stress factors (and even pathologies) that were identified among these professionals. Nevertheless, previous work has uncovered a lack of real-time monitoring and decision technologies that can lead to in-depth understanding of the physiological stress processes and to the development of adequate response mechanisms. Our "silver bullet" to address these challenges is a suite of non-intrusive wearable technologies, as inconspicuous as a t-shirt, capable of gathering relevant information about the individual and disseminating this information through a wireless sensor network. In this paper we will describe the objectives, activities and results of the first two years of the Vital Responder project, depicting how it is possible to address wearable sensing challenges even in very uncontrolled environments. © 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering.

Abstract
The dynamics of image acquisition conditions for gastroenterology imaging scenarios pose novel challenges for automatic computer assisted decision systems. Such systems should have the ability to mimic the tissue characterization of the physicians. In this paper, our objective is to compare some feature extraction methods to classify a Chromoendoscopy image into two different classes: Normal and Potentially cancerous. Results show that LoG filters generally give best classification accuracy among the other feature extraction methods considered.

Abstract
Digital stethoscopes have been drawing the attention of the biomedical engineering community for some time now, as seen from patent applications and scientific publications. In the future, we expect'intelligent stethoscopes' to assist the clinician in cardiac exam analysis and diagnostic, potentiating functionalities such as the teaching of auscultation, telemedicine, and personalized healthcare. In this paper we review the most recent heart sound processing publications, discussing their adequacy for implementation in digital stethoscopes. Our results show a body of interesting and promising work, although we identify three important limitations of this research field: lack of a set of universally accepted heart-sound features, badly described experimental methodologies and absence of a clinical validation step. Correcting these flaws is vital for creating convincing next-generation'intelligent' digital stethoscopes that the medical community can use and trust.