Automated detection of focal EEG signals using features extracted from flexible analytic wavelet transform

•A new method for detection of focal EEG signals has been proposed.•The proposed method is based on the flexible analytic wavelet transform.•The developed method has been compared with the other existing methods.•The obtained classification accuracy by proposed method is 94.41%. Epilepsy is a neurol...

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Bibliographic Details
Published in:Pattern recognition letters 2017-07, Vol.94, p.180-188
Main Authors: Gupta, Vipin, Priya, Tanvi, Yadav, Abhishek Kumar, Pachori, Ram Bilas, Rajendra Acharya, U.
Format: Article
Language:English
Subjects:
Classifiers
EEG
Electroencephalography
Entropy
Epilepsy
FAWT
Feature extraction
Neurological disorders
Signal classification
Statistical analysis
Support vector machines
Wavelet analysis
Wavelet transforms
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Summary:•A new method for detection of focal EEG signals has been proposed.•The proposed method is based on the flexible analytic wavelet transform.•The developed method has been compared with the other existing methods.•The obtained classification accuracy by proposed method is 94.41%. Epilepsy is a neurological disease which is difficult to diagnose accurately. An authentic detection of focal epilepsy will help the clinicians to provide proper treatment for the patients. Generally, focal electroencephalogram (EEG) signals are used to diagnose the epilepsy. In this paper, we have developed an automated system for the detection of focal EEG signals using differencing and flexible analytic wavelet transform (FAWT) methods. The differenced EEG signals are subjected to 15 levels of FAWT. Various entropies namely cross correntropy, Stein’s unbiased risk estimate (SURE) entropy, and log energy entropy are extracted from the reconstructed original signal and 16 sub-band signals. The statistically significant features are obtained from Kruskal–Wallis test based on (p < 0.05). K-nearest neighbor (KNN) and least squares support vector machine (LS-SVM) classifiers with different distances and kernels respectively are used for automated diagnosis. In the proposed methodology, we have achieved classification accuracy of 94.41% in detecting focal EEG signals using LS-SVM classifier with ten-fold cross validation strategy.
ISSN:0167-8655
1872-7344
DOI:10.1016/j.patrec.2017.03.017