Applications of machine learning techniques for the modelling of EEG data for diagnosis of epileptic seizures

Abstract

The aim of this study was to create a simple and effective epileptic seizure detector using EEG data, signal processing and machine learning techniques. This study proposes a simple and effective training set acquisition method for epileptic seizure detection. This training set acquisition method was applied and analyzed in the following three analysis methods. 'Analysis Method 1' uses Multilevel Wavelet Decomposition as a feature vector design process and both Support Vector Machine (SVM) and Extreme Learning Machine (ELM) as feature vector classification methods. 'Analysis Method 2' uses filter data as part of the feature vector design process and both SVM and ELM as feature classification methods. 'Analysis method 3' feeds single-channel, multi-level wavelet decomposition feature vectors, separately, to both SVM and ELM classification methods.