Automated detection of epileptic seizures using mixed-methodology: Wavelet-Chaos-KNN Classifier-Mutual Information

作者：Mirzaei Ahmad^*; Ayatollahi Ahmad; Nasrabadi Ali Motie

来源：Przeglad Elektrotechniczny, 2011, 87(4): 220-223.

• discrete wavelet transform (DWT)
• normalized Shannon entropy (NShEn) normalized spectral entropy (NSEn)
• k-nearest neighbor (KNN)
• mutual information
• encephalography
• epileptic seizures

Electroencephalogram (EEG) is the brain signal that contains the valuable information about different states of the brain. In this study EEG signals are analyzed for evaluating epileptic seizures in these signals and their sub-bands and comparing epileptic states with other states. A discrete wavelet transform is applied for decompose the EEGs into its sub-bands. The chaotic behavior of EEGs is evaluated by means of normalized Shannon and spectral entropies. Entropy method is presented for detection of epileptic seizures through the analysis of EEGs and their sub-bands. At the end the mixture K-nearest neighbor and mutual information method is applied as a classifier to classify the different states in EEGs and their sub-bands. This method is applied to three different groups of EEG signals: 1) healthy states, 2) epileptic states during a seizure-free interval (interictal EEG), 3) epileptic states during a seizure (ictal EEG). The proposed method could classify different states with 99% accuracy.

• 出版日期2011