Automated classification of epileptic eeg signals using wavelet entropies and energies

Bairy, Muralidhar G and Bhat, Shreya and Niranjan, U C (2014) Automated classification of epileptic eeg signals using wavelet entropies and energies. Journal of Medical Imaging and Health Informatics, 4 (6). pp. 868-873. ISSN 2156-7026

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Abstract

Epilepsy is a chronic electrophysiological disorder characterized by repetitive seizures. Electroencephalogram (EEG) signals represent the dynamic condition of the brain. Variations in the EEG signals cannot be predicted by ocular examination. Thus, signal processing methods are used to extract the hidden information from the EEG signals. In this work, three different types of EEG signals (normal, epileptic, background) are used and features are extracted using wavelet entropies and energies. ANOVA is performed followed by classification. A graphical user interface (GUI) is introduced that can help in the automatic classification of the EEG signals. Various classifiers such as decision tree, k-nearest neighbor, support vector machine and fuzzy classifiers are used. Classification accuracy of 97% is achieved using decision tree classifier with GUI.

Item Type: Article
Uncontrolled Keywords: Epilepsy, EEG, wavelet, ANOVA, GUI, DT, SVM
Subjects: Engineering > MIT Manipal > Biomedical
Depositing User: MIT Library
Date Deposited: 06 Jan 2015 09:18
Last Modified: 06 Jan 2015 09:18
URI: http://eprints.manipal.edu/id/eprint/141454

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