Epileptic Seizure Detection Using Discrete Wavelet Transform and Support Vector Machines

Abstract

The electroencephalogram (EEG) signal plays an important role in the detection of epilepsy. The EEG recordings of the ambulatory recording systems generate very lengthy data and the detection of the epileptic activity requires a time- consuming analysis of the entire length of the EEG data by an expert. The aim of this work is to develop a new method for automatic detection of EEG patterns using Discrete wavelet Transform (DWT) and Support Vector Machines (SVM). Our method consists of EEG data collection, feature extraction and classification stages. DWT is used for feature extraction in the principle of time – frequency domain analysis. In classification stage we implement SVM to detect epileptic seizure. SVM provides binary classification between preictal/ictal and interictal states. The study is carried out on EEG recordings of two epileptic patients; two classification models are derived from each patient. The models are then tested on the same patient and the other patient, comparing the specificity, sensitivity and accuracy of each of the models. This model provides high sensitivity compare other detection method.