Development of Seizure Detection Technique Using Inherent Fuzzy Entropy for Complexity Analysis
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Date
2018
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Publisher
MPUAT, Udaipur
Abstract
A neurological condition affecting the central nerve system of people causing recurring
seizure is termed as epilepsy or seizure disorder. A seizure can be described as a brief,
interim disturbance in the electrical activity of the brain and the cause of its occurrence is
the quick firing of too many nerve cells in the brain, causing an electrical storm. Almost
50 million people worldwide have epilepsy and its studies often rely on EEG signals for
analyzing brains behavior during the occurrence of seizure. Many kinds of research around
the world is carried out over past few years to automate the analysis of EEG signal to detect
epilepsy and type of seizure present. This paper presents classification of EEG signals into
healthy / inter-ictal versus ictal EEGs using EMD based fuzzy entropy method and SVM
classifier. In EMD, decomposition of the EEG signal from different epileptic states takes
place to obtain IMFs. Fuzzy entropy reduces the detection time by reducing the data size
of the EEG data without any loss of the information. So here EMD is followed by the
calculation of FuzzyEn of the reconstructed signal obtained from IMFs. Finally, feature
vectors are formed using fuzzy entropy that are applied to non-linear SVM classifier for
classification purpose. The classification accuracies obtained by using proposed method
for Z versus S, O versus S, N versus S, and F versus S when only 50% data is applied
during testing of the classifier are 100%, 99.38%, 98.62%, and 97% respectively
Description
Development of Seizure Detection Technique Using Inherent
Fuzzy Entropy for Complexity Analysis
Keywords
Citation
Tripathi, D.R.and Agarwal, N.