Analysis of EEG signal for seizure detection based on WPT

Electroencephalogram (EEG) is a diagnostic method that provides information about the functioning of the brain. EEG can be used to diagnose the abnormally functioning part of the brain by monitoring electrical activities. An automated system has been proposed to create a computer-based expert opinio...

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Bibliographic Details
Published in:	Electronics letters Vol. 56; no. 25; pp. 1381 - 1383
Main Author:	Arı, A
Format:	Journal Article
Language:	English
Published:	The Institution of Engineering and Technology 10.12.2020
Subjects:	abnormally functioning part automated system binary cases binary classification brain computer‐based expert opinion diagnostic method dispersion entropy EEG signal electrical activities electroencephalography entropy evaluated classification accuracy extracted feature vector feature extraction KNN line length methods medical signal detection medical signal processing multiclass cases multiclass classification evaluated accuracy neighbour classifiers objective view pattern classification public EEG seizure dataset seizure detection signal classification Special Issue: Current Trends in Cognitive Science and Brain Computing Research and Applications support vector machine support vector machines SVM wavelet packet wavelet transforms WPT computer-based expert opinion wavelet transforms KNN SVM entropy feature extraction neighbour classifiers automated system multiclass cases WPT line length methods binary classification evaluated classification accuracy electroencephalography pattern classification multiclass classification evaluated accuracy diagnostic method support vector machines abnormally functioning part medical signal detection brain EEG signal wavelet packet signal classification medical signal processing support vector machine seizure detection public EEG seizure dataset electrical activities binary cases dispersion entropy objective view extracted feature vector
ISSN:	0013-5194, 1350-911X, 1350-911X
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Summary:	Electroencephalogram (EEG) is a diagnostic method that provides information about the functioning of the brain. EEG can be used to diagnose the abnormally functioning part of the brain by monitoring electrical activities. An automated system has been proposed to create a computer-based expert opinion needed in the detection of epilepsy and to capture a more objective view. To this end, the approximation and detail coefficients of EEG signals are calculated by using the wavelet packet transform (WPT). The coefficients were subjected to feature extraction using dispersion entropy and line length methods. The extracted feature vector has been applied as input to the support vector machine (SVM) and k-nearest neighbour (KNN) classifiers. The proposed method was tested using the public EEG seizure dataset created by the University of Bonn. In this study, the dataset was evaluated in two different ways as binary cases and multiclass cases. Evaluated classification accuracy was 100% for binary classification with SVM. For multiclass classification evaluated accuracy was 99.85% with KNN. The proposed method was compared with other methods in the literature using the same dataset. The comparison results provide the superiority of the proposed method.
ISSN:	0013-5194 1350-911X 1350-911X
DOI:	10.1049/el.2020.2701