Abstract

Abstract

Abstract
Epileptic seizures induce, in many situations, non-coordinated movement in the patient's body. This movement is relevant for seizure identification. Nevertheless, quantification of this information has not been an object of much attention of scientific community. In this paper, we present our efforts in developing a new approach to quantify movement of patients during epileptic seizures. We present a new method to extract the course of the quantified movement of different parts of the body of epileptic patients during seizures. This information constitutes a quantified movement pattern for each seizure that can be compared with clinical "standard" movement patterns and may help clinicians in the seizure classification.

Abstract

Abstract
In this paper, we present a multimedia system named BioBeAMS (Bio-signal Behavior Attachment Multimedia System) for assessment of attachment organizations and heart rate in Strange Situation experimental procedure for mother to child attachment study (a well known psychological procedure). Attachment patterns assessed through this procedure have been conceived as different strategies to regulate the access to relevant attachment information and to process distress-related information. The psycho-physiological activation led by emotional processing of attachment experiences during the Strange Situation procedure may be a critical factor for understanding the subject's psychological attachment strategies. BioBeAMS is an application, which integrates the bio-signal, video/audio of the psychological experimental procedure and the rater's coding together, running in the MS Window 95 environment on a PC. Using this system, we can acquire ECG signal and capture video/audio in a synchronous way, It also enables the raters to subsequently analyze and evaluate psychological and physiological events during review to study possible relations.

Abstract
Functional magnetic resonance imaging (fMRI) is a medical imaging technique used to characterize brain physiological activity, usually presented as 3D volumes in function of time. Our previous work in nonlinear association studies in electroencephalogram (EEG) time series enabled us to identify EEG features relevant for clinical diagnosis. The use of a similar approach in fMRI (adapted for 3D time series) is both appealing and new. Such time series analysis however imposes challenging requirements regarding computational power and medical image management. In this paper we propose the use of Grid computing to cope with the demanding fMRI multi-voxel association analysis workflow and present a working prototype. The system, implemented using the gLite middleware, provides the necessary support to manage brain images and run different non-linear fMRI analysis methods.