Epileptic Seizure Detection using Simulated Annealing based Optimal Feature Subset Selection with Kernel Extreme Learning Machine Classification Model

Abstract

Electroencephalogram (EEG) signal based epileptic seizure detection is a hot research area, which identifies the non-stationary progresses of brain actions. Typically, the epilepsy is detected by doctors based on the visual examination of EEG signals consumes more time and highly sensitive to noise. Presently, machine learning (ML) techniques finds useful to predict the existence of epileptic seizure from EEG signals. This paper aims to develop a ML based Epileptic seizure detection model in EEG signals. The proposed model involves three major processes namely preprocessing, feature selection and classification. Initially, the EEG signal undergoes preprocessing in two ways namely data normalization, and class labeling. Next, simulated annealing (SA) is applied as a feature selector to choose an optimal set of features. At last, kernel extreme learning machine (KELM) based classification process takes place to detect and classify the existence of EEG. A detailed simulation analysis takes place to inspect the performance of the SA-KELM model. The experimental outcome stated that the SA-KELM model has offered maximum detection performance under the classification of multiple classes with the maximum sensitivity of 57.27%, specificity of 89.32%, accuracy of 82.91%, F-score of 57.29% and kappa of 46.62%.