Quantitative EEG based on Renyi Entropy for Epileptic Classification

Analysis on Electroencephalogram (EEG) signal can provide important information related to the clinical pathology of epilepsy. Detecting the onset, prediction and type of seizures based on EEG signals is very important to determine an appropriate treatment for the patients. However, EEGs have the high complexity with non-linear and non-stationary characteristics; hence, an analysis will be very difficult to do through a visual inspection. Signal processing applications are, therefore, needed to make the interpretation easier. In this study, we proposed a method for EEG analysis based on signal complexity for the epileptic EEG classification. The Renyi entropy was used to extract the data of EEG features, which consist of seizure, interictal and normal features. Then, these features became the input to a classification algorithm. SVM (Support vector machine) classifier was applied to determine the type of that epileptic EEG signal and achieved accuracy of 85 %. This study can be a reference for neurology as an efficient method for epileptic EEG classification

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