Analysis of eeg signals for detection of epileptic seizure using hybrid feature set

Abstract

Epileptic Seizures occur as a result of certain electrical action in the brain. This makes the patient behave abnormally for a limited amount of time. The electrical activity can be measured with the help electrodes attached to different areas of the scalp to capture the EEG signals. Usually, the signals from the aforementioned device are interpreted by the specialists who specialize in this very thing but their detection is susceptible to errors which prove fatal in some cases. This paper provides an automated system which will detect epileptic seizure without involving an expert opinion. The proposed system goes through a four step process i.e. pre-processing, where the data is organized to suit the system processing and noise is removed. Then temporal and spectral feature extraction is performed. The system then applies the feature selection procedure to extract best set of features which are finally passed to the next phase for classification of EEG signals as normal or abnormal. The suggested system is established on a publicly open dataset and provides an average accuracy of 86.93 %.

Keywords Feature Selection, Gaussian Mixture Model, Epileptic Seizure, Approximate Entropy, Seizure Detection