Automated classification of epileptic electroencephalogram signals using wavelet entropies and energies

G. Muralidhar Bairy; Shreya Bhat; U. C. Niranjan

doi:10.1166/jmihi.2014.1335

Automated classification of epileptic electroencephalogram signals using wavelet entropies and energies

G. Muralidhar Bairy, Shreya Bhat, U. C. Niranjan

Department of Biomedical Engineering, Manipal Institute of Technology, Manipal

Research output: Contribution to journal › Article › peer-review

4 Citations (Scopus)

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 neighbour, support vector machine and fuzzy classifiers are used. Classification accuracy of 97% is achieved using decision tree classifier with GUI.

Original language	English
Pages (from-to)	868-873
Number of pages	6
Journal	Journal of Medical Imaging and Health Informatics
Volume	4
Issue number	6
DOIs	https://doi.org/10.1166/jmihi.2014.1335
Publication status	Published - 01-01-2014

All Science Journal Classification (ASJC) codes

Radiology Nuclear Medicine and imaging
Health Informatics

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10.1166/jmihi.2014.1335

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Automated classification of epileptic electroencephalogram signals using wavelet entropies and energies

Abstract

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