Patient-Specific Seizure Detection Method using Hybrid Classifier with Optimized Electrodes.

Abstract

In this paper the EEG signal is analyzed by reconstructing the time series EEG signal in High dimensional Phase Space. The computational complexity in higher dimension is reduced by Principal Component Analysis for the High dimensional Phase Space output. Poincare sectioning is done for the first and second Principal Components (PCs). The intersection points of PCs and the Poincare section are collected and used for features calculation. Two layer of classification is done using SVM as first layer and Naive Bayes as second layer. The proposed methodology is evaluated using the CHB-MIT database for 23 subjects. The results are obtained using different channel combinations of EEG signal and highest of 95.63% accuracy, 95.7% sensitivity and 96.55% specificity is obtained for 12 electrode combinations which include electrodes from parietal and occipital lobes. This infers that most of the subjects have dysfunction in hearing (controlled by parietal) and vision (controlled by occipital) during the time of seizure. This GUI has channel selection option and seizure detection for every channel (23) for every 1s.