alexa Automatic Seizure Onset Detection in Long Term Pediatric EEG Signals | OMICS International | Abstract
ISSN: 0974-7230

Journal of Computer Science & Systems Biology
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Research Article

Automatic Seizure Onset Detection in Long Term Pediatric EEG Signals

Saeed MT*, Zuhaib M, Khan YU and Azeem MF

Department of Electrical Engineering, Aligarh Muslim University, India

*Corresponding Author:
Tayyab Saeed
Department of Electrical Engineering
Aligarh Muslim University, India
Tel: +918791233285
E-mail: [email protected]

Received date: May 13, 2016; Accepted date: June 28, 2016; Published date: July 04, 2016

Citation: Saeed MT, Zuhaib M, Khan YU, Azeem MF (2016) Automatic Seizure Onset Detection in Long Term Pediatric EEG Signals. J Comput Sci Syst Biol 9: 125-131. doi:10.4172/jcsb.1000230

Copyright: © 2016 Saeed MT, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Despite regular medication management, many patients continue to have seizures. Thus, there is a need of more tailored therapy and consequently more sophisticated and accurate seizure diagnostic tools. Background EEG activity is used by physicians for finding information regarding dysfunction of associated central nervous system and risk of seizures. Considering its importance, background activity is exploited in this work for computation of relative entropy, Cauchy-Schwartz divergence, change in median absolute deviation, change in normalized coefficient of variation and change in Katz fractal dimension. The results are highly promising and comparative study suggests that considering background activity outperforms the other techniques.

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