Abstract
Epilepsy being one of the most prevalent neurological disorders, affecting approximately 50 million people worldwide, and with almost 30-40% of patients experiencing partial epilepsy being nonresponsive to medication, epilepsy surgery is widely accepted as an effective therapeutic option. Presurgical evaluation has advanced significantly using noninvasive techniques based on video monitoring, neuroimaging, and electrophysiological and neuropsychological tests; however, certain clinical settings call for invasive intracranial recordings such as stereoelectroencephalography (SEEG), aiming to accurately map the eloquent brain networks involved during a seizure. Most of the current presurgical evaluation procedures focus on semiautomatic techniques, where surgery diagnosis relies immensely on neurologists' experience and their time-consuming subjective interpretation of semiology or the manifestations of epilepsy and their correlation with the brain's electrical activity. Because surgery misdiagnosis reaches a rate of 30%, and more than one-third of all epilepsies are poorly understood, there is an evident keen interest in improving diagnostic precision using computer-based methodologies that in the past few years have shown near-human performance. Among them, deep learning has excelled in many biological and medical applications, but has advanced insufficiently in epilepsy evaluation and automated understanding of neural bases of semiology. In this paper, we systematically review the automatic applications in epilepsy for human motion analysis, brain electrical activity, and the anatomoelectroclinical correlation to attribute anatomical localization of the epileptogenic network to distinctive epilepsy patterns. Notably, recent advances in deep learning techniques will be investigated in the contexts of epilepsy to address the challenges exhibited by traditional machine learning techniques. Finally, we discuss and propose future research on epilepsy surgery assessment that can jointly learn across visually observed semiologic patterns and recorded brain electrical activity.

Abstract

Abstract
Deep Brain Stimulation (DBS) is the effective surgical treatment for drug-refractory movement disorders. In order to improve the therapeutic outcome precise anatomic location of electrodes must be achieved. Thus, neurologists can achieve better clinical decisions and take a more careful selection of the best stimulation parameters for DBS. In this paper, we present a system that accurately obtains the 3D positions of DBS electrodes relative to anatomical structures. The latter is based on the segmentation of deep brain structures and on a multimodal imaging approach. In this study, we examined 16 patients undergoing DBS (8 with Parkinson` s disease and 8 with dystonia). A "neuroscientist friendly" graphic user interface (GUI) was designed to support the processing pipeline to precisely detect the electrodes from the DBS lead. Using this system, we obtained the electrodes position and compared them with the ones manually calculated by an experienced physician. The differences observed were less than a voxel size for 89.9% of the cases and the automated procedure takes less 97.5% time than the manual procedure (1min vs 40min). The resulting masks were congruent in shape and position with the corresponding areas in the individuals' space. Using our automatic segmentation pipeline, clinicians save 77% of their time when compared with a manual segmentation (1.20min vs 5.26min). Both structures and electrodes masks were warped to the MNI space in order to provide a common reference space, for the clinical interpretations.

Abstract
Video can be a valuable source of information for monitoring first responders during operations in the field. In this paper we propose the MonitorMe, an application that supports online video monitoring of first responders. MonitorMe allows capturing video from a personal perspective, using a smartphone camera, and sending it to a remote observer. In order to reduce battery consumption and bandwidth usage, MonitorMe modulates the video frame rate according to the user activity/speed. The latter are estimated using the smartphone built-in accelerometer. The results have shown the potential of MonitorMe as a reliable non-GPS solution, which can be used for remote online monitoring of first responders in action. © 2013 IEEE.

Abstract
The communication between two neurons is established by endogenous chemical particles aggregated in vesicles that move along the axons. It is known that an abnormal transport of these vesicles is correlated with neurodegenerative diseases. The quantification of the dynamics of vesicles movement can therefore be a window to study early detection of such diseases. Nevertheless, most of the studies in the literature rely on manual tracking techniques. In this paper we present a novel methodology for quantifying neurotransmitter vesicle dynamics by using a combination of image tracking and classification algorithms. We use confocal microscopy videos of living neurons to detect and classify vesicles using support vector machine (SVM), while motion is extracted via global nearest neighbor (GNN) tracking approach. Results of the classification algorithm are presented and compared to a ground truth dataset defined by experts. Sensitivity of 90% and specificity of 97% were obtained at a much lower computational cost than an established method from the literature (0.24s/frame vs. 125s/frame). These preliminary results suggest the great potential of the method and tool we have been developing for single particle movement dynamics measure in living cells.

Abstract
Background and Objectives: In spite of the existence of a multitude of techniques that allow the estimation of stress from physiological indexes, its fine-grained assessment is still a challenge for biomedical engineering. The short-term assessment of stress condition overcomes the limits to stress characterization with long blocks of time and allows to evaluate the behaviour change in real-world settings and also the stress level dynamics. The aim of the present study was to evaluate time and frequency domain and nonlinear heart rate variability (HRV) metrics for stress level assessment using a short-time window. Methods: The electrocardiogram (ECG) signal from 14 volunteers was monitored using the Vital Jacketml while they performed the Trier Social Stress Test (TSST) which is a standardized stress-inducing protocol. Window lengths from 220 s to 50 s for HRV analysis were tested in order to evaluate which metrics could be used to monitor stress levels in an almost continuous way. Results: A sub-set of HRV metrics (AVNN, rMSSD, SDNN and pNN20) showed consistent differences between stress and non-stress phases, and showed to be reliable parameters for the assessment of stress levels in short-term analysis. Conclusions: The AVNN metric, using 50 s of window length analysis, showed that it is the most reliable metric to recognize stress level across the four phases of TSST and allows a fine-grained analysis of stress effect as an index of psychological stress and provides an insight into the reaction of the autonomic nervous system to stress.