Comparison of different classification methods for the preictal stage detection in EEG signals.

Abstract

In this study, we present an evaluation and comparison of the widely used linear discriminant analysis, k-Nearest neighbor algorithm, support vector machines, multi-layer perceptron neural network and decision tree classification performances for preictal stage detection in EEG signal. Analysis has been done for fourteen patients with epilepsy. Firstly, 26 features are extracted from time domain, frequency domain and power spectrum. The feature set dimensionality has been reduced from 26 to 8 using Principal Component Analysis. Finally, five classifiers have been employed to classify EEG signals into normal, ictal and preictal stages. The classification is performed for patient-specific. We emphasized the importance of the analysis of preictal stage for seizure prediction. According to classification results and ROC analysis, Linear Discriminant Analysis and Support Vector Machines have better performances than others. LDA achieved the highest average sensitivity with 88.06% in the preictal stage detection process. The results are very promising and contributing to possible guide for future seizure detection and prediction studies.