A Novel Method for Epileptic EEG Classification Using DWT, MGA, and ANFIS: A Real Time Application to Cardiac Patients with Epilepsy
The automatic diagnosis of heart patients with epilepsy by reviewing the EEG recording is highly necessary. It aims to enhance the significant statistical parameters. In this paper a composite method is proposed for seizure classification of cardiac patients. Firstly DWT is employed to analyze the EEG data and obtain the time and frequency domain features. Second, the extracted features were inputted to the ANFIS network to classify the seizure EEG and seizure free EEG signals. Third to improve the statistical performances a modified genetic algorithm (MGA) is used to optimize the classifiers. Sensitivity (SEN), Specificity (SPE), Accuracy (ACC), metric G-mean and Average detection Ratio (ADR) is used to evaluate the performance of this method. The SEN of 99.73%, SPE of 99.12%, ACC of 99.35%, G-mean 99.42% and ADR of 99.43% are yielded on the real patient specific EEG database. The comparison with other detection methods shows the superior performance of this method, which indicates its potential for detecting seizure events in clinical practice of heart patients.
KeywordsEEG Epilepsy DWT MGA ANFIS
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