Epileptic seizure classification using novel entropy features applied on maximal overlap discrete wavelet packet transform of EEG signals

Using electroencephalography for diagnosis of seizure attacks has been in a great attention as it records abnormal electrical activities of the brain. This paper proposes a novel technique for diagnosis of epileptic seizures based on non-linear entropy features extracted from maximal overlap discret...

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Bibliographic Details
Published in2017 7th International Conference on Computer and Knowledge Engineering (ICCKE) pp. 390 - 395
Main Authors Ahmadi, Amirmasoud, Tafakori, Shiva, Shalchyan, Vahid, Daliri, Mohammad Reza
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2017
Subjects
Discrete wavelet transforms
Electroencephalography
Entropy
Epileptic Seizure classification
Feature extraction
Maximal Overlap Discrete Wavelet packets transform (MODWPT)
Wavelet coefficients
Wavelet packets
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DOI10.1109/ICCKE.2017.8167910

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Summary:Using electroencephalography for diagnosis of seizure attacks has been in a great attention as it records abnormal electrical activities of the brain. This paper proposes a novel technique for diagnosis of epileptic seizures based on non-linear entropy features extracted from maximal overlap discrete wavelet packet transform (MODWPT) of EEG signals. Discriminative features are selected by a t-test criterion and used for the classification with two different classifiers. The proposed method is evaluated and compared to the previous methods in EEG seizure classification by using a publically available EEG dataset with different healthy and seizure suffering subjects. The obtained results show the superiority of the proposed method over the previous techniques in classification performance.
DOI:10.1109/ICCKE.2017.8167910