A case study on Discrete Wavelet Transform based Hurst exponent for epilepsy detection.
Epileptic seizures are manifestations of epilepsy. Careful analysis of EEG records can provide valuable insight and improved understanding of the mechanism causing epileptic disorders. The detection of epileptic form discharges in EEG is an important component in the diagnosis of epilepsy. As EEG signals are non-stationary, the conventional frequency and time domain analysis does not provide better accuracy. So, in this work an attempt has been made to provide an overview of the determination of epilepsy by implementation of Hurst exponent (HE)-based discrete wavelet transform techniques for feature extraction from EEG data sets obtained during ictal and pre ictal stages of affected person and finally classifying EEG signals using SVM and KNN Classifiers. The The highest accuracy of 99% is obtained using SVM.
['Madan|Saiby|S|', 'Srivastava|Kajri|K|', 'Sharmila|A|A|', 'Mahalakshmi|P|P|']
D000465:Algorithms D004569:Electroencephalography D004827:Epilepsy D006801:Humans D060388:Support Vector Machine D058067:Wavelet Analysis
discrete wavelet transform Hurst exponent Epilepsy K-nearest neighbour support vector machine