Abstract
Although there is a growing number of scientific papers describing classification of in-body images, most of it is based on traditional colour histograms. In this paper we explain why these might not be the most adequate visual features for in-body image classification. Based on a colour dynamic range maximization criterion, we propose a methodology for creating more adequate colour histograms, testing it on a vital-stained magnification endoscopy scenario.

Abstract
We consider the separation of sources when only one movable sensor is available to record a set of mixtures at distinct locations. A single mixture signal is acquired, which is firstly segmented. Then, based on the assumption that the underlying sources are temporally periodic, we align the resulting signals and form a measurement vector on which source separation can be performed. We demonstrate that this approach can successfully recover the original sources both when working with simulated data, and for a real problem of heart sound separation.

Abstract
Digital stethoscopes are medical devices that can collect, store and sometimes transmit acoustic auscultation signals in a digital format. These can then be replayed, sent to a colleague for a second opinion, studied in detail after an auscultation, used for training or, as we envision it, can be used as a cheap powerful tool for screening cardiac pathologies. In this work, we present the design, development and deployment of a prototype for collecting and annotating auscultation signals within real hospital environments. Our main objective is not only pave the way for future unobtrusive systems for cardiac pathology screening, but more immediately we aim to create a repository of annotated auscultation signals for biomedical signal processing and machine learning research. The presented prototype revolves around a digital stethoscope that can stream the collected audio signal to a nearby tablet PC. Interaction with this system is based on two models: a data collection model adequate for the uncontrolled hospital environments of both emergency room and primary care, and a data annotation model for offline metadata input. A specific data model was created for the repository. The prototype has been deployed and is currently being tested in two Hospitals, one in Portugal and one in Brazil.

Abstract
This work presents an investigation of the potential benefits of customizing the analysis of long-term ECG signals, collected from individuals using wearable sensors, by incorporating small amount of data from these individuals in the training set of our classifiers. The global training dataset selected was from the MIT-BIH Arrhythmias Database. This proposal is validated on long-term ECG recordings collected via wearable technology in unsupervised environments, as well on the MIT-BIH Normal Sinus Rhythm Database. Results illustrate that heartbeat classification performance could improve significantly if short periods of data (e.g., data from the first 5-minutes of every 2 hours) from the specific individual are regularly selected and incorporated into the global training dataset for training a customized classifier. © 2011 IEEE.

Abstract
In this paper we compare the classification accuracy of using compressed domain color (CDC) descriptors versus traditional full decoded images, for the purposes of topographic classification of wireless capsule endoscopy images. Results using a dataset of 26469 images, divided into stomach, small intestine and large intestine show a difference in classification accuracy below 1%. We also show that errors are mostly located near zone transitions (the pylorus and the ileocecal valve) and motivate the need for other visual descriptors (e. g. shape, motion) for addressing these specific areas. We conclude we can use the advantages of CDC in this type of classification with minor accuracy sacrifice.

Abstract
In this paper we associate features obtained from ECG signals with the expected levels of stress of real firefighters in action when facing specific events such as fires or car accidents. Five firefighters were monitored using wearable technology collecting ECG signals. Heart rate and heart rate variability features were analyzed in consecutive 5-min intervals during several types of events. A questionnaire was used to rank these types of events according to stress and fatigue and a measure of association was applied to compare this ranking to the ECG features. Results indicate associations between this ranking and both heart rate and heart rate variability features extracted in the time domain. Finally, an example of differences in inter personal responses to stressful events is shown and discussed, motivating future challenges within this research field.