Epileptic seizure detection using dynamic wavelet network
Epileptic seizures are manifestations of epilepsy. Careful analyses of the electroencephalograph (EEG) records can provide valuable insight and improved understanding of the mechanisms causing epileptic disorders. The detection of epileptiform discharges in the EEG is an important component in the diagnosis of epilepsy. Wavelet transform is particularly effective for representing various aspects of non-stationary signals such as trends, discontinuities, and repeated patterns where other signal processing approaches fail or are not as effective. Through wavelet decomposition of the EEG records, transient features are accurately captured and localized in both time and frequency context. This paper deals with a novel method of analysis of EEG signals using discrete wavelet transform, and classification using ANN. EEG signals were decomposed into the frequency sub-bands using wavelet transform. Then these sub-band frequencies were used as an input to an ANN with two discrete outputs: normal and epileptic. In this study, FEBANN and DWN based classifiers were developed and compared in relation to their accuracy in classification of EEG signals. The comparisons between the developed classifiers were primarily based on analysis of the ROC curves as well as a number of scalar performance measures pertaining to the classification. The DWN-based classifier outperformed the FEBANN based counterpart. Within the same group, the DWN-based classifier was more accurate than the FEBANN-based classifier.
epileptic seizure electroencephalogram (eeg) discrete wavelet transform (dwt) feedforward error backpropagation artificial neural network (febann) dynamic wavelet network (dwn)