Statistical sampling and feature selection for epilepsy pattern recognition

Abstract

Epilepsy is one of the most common neurological diseases that has broad spectrum of debilitating medical and social consequences. The automatic forecasting and detecting systems are vitally important, since they allow patients to avoid dangerous activities in advance of the seizure. We present some methods of feature extraction and selection for detecting the epileptiform activity in electroencephalography signals, based on the processing of a non-stationary signal. The proposed approach is based on the application of the Discrete Wavelet Transform (DWT) and signal processing techniques in order to create the feature vector. Afterwards, the principal component analysis and support vector machine techniques are used in order to reduce the dimensionality of the feature vector.