Authors:
Fadi N. Karameh
;
Mohamad Awada
;
Firas Mourad
;
Karim Zahed
;
Ibrahim Abou-Faycal
and
Ziad Nahas
Affiliation:
American University of Beirut, Lebanon
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 disin
hibition 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)