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Classification of Epileptic EEG Using Wavelet Transform & Artificial Neural Network

Author(s): Ashish Raj | Manoj Kumar Bandil | D B V Singh | Dr. A.K Wadhwani

Journal: International Journal of Advanced Research in Computer Science
ISSN 0976-5697

Volume: 04;
Issue: 09;
Start page: 165;
Date: 2013;
Original page

Keywords: EEG | Epilepsy | Wavelet | transform | Feature | Extraction | Neural | network | Backpropogation | Neural | Network.

Epilepsy is a neurological disorder with prevalence of about 1-2% of the world’s population. The hallmark of epilepsy is recurrent seizures termed "epileptic seizures". Human Brain is the most complex organ among all the systems in the human body, also the most remarkable one. It exhibits rich spatiotemporal dynamics. Electroencephalography (EEG) signal is the recording of spontaneous electrical activity of the brain over a small period of time . The term EEG refers that the brain activity emits the signal from head and being drawn. It is produced by bombardment of neurons within the brain. EEG signal provides valuable information of the brain function and neurobiological disorders as it provides a visual display of the recorded waveform and allows computer aided signal processing techniques to characterize them. This gives a prime motivation to apply the advanced digital signal processing techniques for analysis of EEG signals. The main objective of our research is to analyze the acquired EEG signals using signal processing tools such as wavelet transform and classify them into di?erent classes. The features from the EEG are extracted using statistical analysis of parameters obtained by wavelet transform. After feature extraction secondary goal is to improve the accuracy of classi?cation. Total 300 EEG data subjects were analyzed. These data were grouped in three classes’ i.e, Normal patient class, Epileptic patient class and epileptic patient during non-seizure zone respectively. In order to achieve this we have applied a backpropgation based neural network classifier. After feature extraction secondary goal is to improve the accuracy of classi?cation. 100 subjects from each set were analysed for feature extraction and classification and data were divided in training, testing and validation of proposed algorithm.
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