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Epileptic Seizure Prediction using Statistical Behavior of Local Extrema and Fuzzy Logic System

by Hamid Niknazar, Keivan Maghooli, Ali Motie Nasrabadi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 113 - Number 2
Year of Publication: 2015
Authors: Hamid Niknazar, Keivan Maghooli, Ali Motie Nasrabadi
10.5120/19799-1578

Hamid Niknazar, Keivan Maghooli, Ali Motie Nasrabadi . Epileptic Seizure Prediction using Statistical Behavior of Local Extrema and Fuzzy Logic System. International Journal of Computer Applications. 113, 2 ( March 2015), 24-30. DOI=10.5120/19799-1578

@article{ 10.5120/19799-1578,
author = { Hamid Niknazar, Keivan Maghooli, Ali Motie Nasrabadi },
title = { Epileptic Seizure Prediction using Statistical Behavior of Local Extrema and Fuzzy Logic System },
journal = { International Journal of Computer Applications },
issue_date = { March 2015 },
volume = { 113 },
number = { 2 },
month = { March },
year = { 2015 },
issn = { 0975-8887 },
pages = { 24-30 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume113/number2/19799-1578/ },
doi = { 10.5120/19799-1578 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:49:55.846526+05:30
%A Hamid Niknazar
%A Keivan Maghooli
%A Ali Motie Nasrabadi
%T Epileptic Seizure Prediction using Statistical Behavior of Local Extrema and Fuzzy Logic System
%J International Journal of Computer Applications
%@ 0975-8887
%V 113
%N 2
%P 24-30
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Epileptic seizures are generated by abnormal activity of neurons. EEG-based epileptic seizure prediction could be a key to improve life style of patients that suffer from drug-resistance epilepsy. In this study, we propose a fuzzy logic system to predict epileptic seizures by using statistical behavior of local extrema (SBLE) features and a rule-based fuzzy system. Two approaches are considered to evaluate the proposed method. First approach is patient-dependent, which requires EEG data in preictal and interictal state. Second approach is leave one out (LOO) technique to evaluate generalizability of the method. Applied to the Freiburg EEG dataset, it was found that the method has good performance for most of the patients of this database. In the patient-dependent approach, sensitivity of 84% with no false alarm and sensitivity of 94. 15% with a false alarm rate of 0. 1 were achieved. LOO evaluation approach obtained a sensitivity of 79. 38% with a false alarm rate of 0. 049. It is remarkable that for many of patients, the proposed method achieved sensitivity of 100% with no false alarm in both of evaluation approaches. This study showed that application of SBLE features as inputs of fuzzy logic system is a suitable way to track EEG changes leading to epileptic seizures.

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Index Terms

Computer Science
Information Sciences

Keywords

Epilepsy Fuzzy Logic SBLE Prediction Genetic Algorithm

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