Identification of epileptic seizures using Hilbert transform and learning vector quantization based classifier

摘要

This work describes the development of a computer-aided diagnostic model for the analysis and classification of EEG signals. The main objective of this study is to achieve an accurate as well as timely classification model which would help in the detection of epileptic EEG signals. This is very important as the patient suffering from epilepsy should receive proper medical attention hours before seizures occur. Thus the importance of fast and accurate analysis of different biomedical signals is growing at an ever increasing rate. In this study we have developed a feature extractor which when integrated with a classifier based on the Learning Vector Quantization (LVQ) algorithm classifies EEG signals into two categories viz. healthy and epileptic. The feature extractor uses the Hilbert Transform to convert real-time series EEG signals into an analytic signal which makes it easier to perform the requisite analysis. 5 sets of EEG signals from a publicly available EEG time series database were used to develop the proposed model on MATLAB. The average accuracy of classification of our proposed methodology is obtained to be as high as 89.31%.