Epileptic Seizures Detection Using DCT-II and KNN Classifier in Long-Term EEG Signals

Abstract

     Epilepsy is one of the most common diseases of the nervous system around the world, affecting all age groups and causing seizures leading to loss of control for a period of time. This study presents a seizure detection algorithm that uses Discrete Cosine Transformation (DCT) type II to transform the signal into frequency-domain and extracts energy features from 16 sub-bands. Also, an automatic channel selection method is proposed to select the best subset among 23 channels based on the maximum variance. Data are segmented into frames of  one Second length without overlapping between successive frames. K-Nearest Neighbour (KNN) model is used to detect those frames either to ictal (seizure) or interictal (non-seizure) based on Euclidean distance. The experimental results are tested on 21 patients included in the CHB-MIT dataset. The average F1-score was found to be 93.12, whereas the False-Positive Rate (FPR) average was determined to be 0.07.