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Authors: Fadi N. Karameh ; Mohamad Awada ; Firas Mourad ; Karim Zahed ; Ibrahim Abou-Faycal and Ziad Nahas

Affiliation: American University of Beirut, Lebanon

ISBN: 978-989-758-011-6

Keyword(s): Neuronal Modeling, EEG, Electroconvulsive Therapy, Kalman Filtering, Estimation.

Related Ontology Subjects/Areas/Topics: Applications and Services ; Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Computer Vision, Visualization and Computer Graphics ; Detection and Identification ; Electromagnetic Fields in Biology and Medicine ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Medical Image Detection, Acquisition, Analysis and Processing ; Methodologies and Methods ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Physiological Processes and Bio-Signal Modeling, Non-Linear Dynamics ; Sensor Networks ; Signal Processing ; Soft Computing ; Theory and Methods

Abstract: Electroconvulsive therapy (ECT) is a procedure that involves the induction of seizures in the brain of patients with severe psychiatric disorders. The efficacy and therapeutic outcome of electrically-induced seizures is dependent upon both the stimulus intensity and the electrode placement over the scalp, with potentially significant memory loss as side effect. Over the years, ECT modeling aimed to understand current propagation in the head medium with increasingly realistic geometry and conductivity descriptions. The utility of these models remain limited since seizure propagation in the active neural tissue has largely been ignored. Accordingly, a modeling framework that combines head conductivity models with active neural models to describe observed EEG signals under ECT is highly desirable. We present herein a simplified multi-area active neural model that describes (i) the transition from normal to seizure states under external stimuli with particular emphasis on disinh ibition and (ii) the initiation and propagation of seizures between multiple connected brain areas. A simulation scenario is shown to qualitatively resemble clinical EEG recordings of ECT. Fitting model param- eters is then performed using modern nonlinear state estimation approaches (cubature Kalman filters). Future work will integrate active models with passive volume conduction approaches to explore seizure induction and propagation. (More)

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Paper citation in several formats:
N. Karameh, F.; Awada, M.; Mourad, F.; Zahed, K.; Abou-Faycal, I. and Nahas, Z. (2014). Modeling of Neuronal Population Activation under Electroconvulsive Therapy.In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2014) ISBN 978-989-758-011-6, pages 229-238. DOI: 10.5220/0004804002290238

author={Fadi N. Karameh. and Mohamad Awada. and Firas Mourad. and Karim Zahed. and Ibrahim Abou{-}Faycal. and Ziad Nahas.},
title={Modeling of Neuronal Population Activation under Electroconvulsive Therapy},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2014)},


JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2014)
TI - Modeling of Neuronal Population Activation under Electroconvulsive Therapy
SN - 978-989-758-011-6
AU - N. Karameh, F.
AU - Awada, M.
AU - Mourad, F.
AU - Zahed, K.
AU - Abou-Faycal, I.
AU - Nahas, Z.
PY - 2014
SP - 229
EP - 238
DO - 10.5220/0004804002290238

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