NDLI: Hidden Markov chain modeling for epileptic networks identification

Content Provider	IEEE Xplore Digital Library
Author	Le Cam, S. Louis-Dorr, V. Maillard, L.
Copyright Year	2013
Description	Author affiliation: CRAN, Univ. de Lorraine, Vandoeuvre les Nancy, France (Le Cam, S.; Louis-Dorr, V.; Maillard, L.)
Abstract	The partial epileptic seizures are often considered to be caused by a wrong balance between inhibitory and excitatory interneuron connections within a focal brain area. These abnormal balances are likely to result in loss of functional connectivities between remote brain structures, while functional connectivities within the incriminated zone are enhanced. The identification of the epileptic networks underlying these hypersynchronies are expected to contribute to a better understanding of the brain mechanisms responsible for the development of the seizures. In this objective, threshold strategies are commonly applied, based on synchrony measurements computed from recordings of the electrophysiologic brain activity. However, such methods are reported to be prone to errors and false alarms. In this paper, we propose a hidden Markov chain modeling of the synchrony states with the aim to develop a reliable machine learning methods for epileptic network inference. The method is applied on a real Stereo-EEG recording, demonstrating consistent results with the clinical evaluations and with the current knowledge on temporal lobe epilepsy.
Starting Page	4354
Ending Page	4357
File Size	370727
Page Count	4
File Format	PDF
ISBN	9781457702167
ISSN	1557170X
DOI	10.1109/EMBC.2013.6610510
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-03
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models Delays Epilepsy Hippocampus Brain modeling Hidden Markov Chain Temporal Lobe Epilepsy Stereo-EEG Network Inference Bayesian Approach
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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