Effective electroencephalogram based epileptic seizure detection using support vector machine and statistical moment’s features

Abstract

Epilepsy is one of the widespread disorders. It is a noncommunicable disease that affects the human nerve system. Seizures are abnormal patterns of behavior in the electricity of the brain which produce symptoms like losing consciousness, attention or convulsions in the whole body. This paper demonstrates an effective electroencephalogram (EEG) based seizure detection method using discrete wavelet transformation (DWT) for signal decomposition to extract features. An automatic channel selection method was proposed by the researcher to select the best channel from 23 channels based on maximum variance value. The records were segmented into a nonoverlapping segment with long 1-S. The support vector machine (SVM) model was used to automatically detect segments that contain seizures, using both frequency and time domain statistical moment features. The experimental result was obtained from 24 patients in CHB-MIT database. The average accuracy is 94.1, sensitivity is 93.5, specificity is 94.6 and the false positive rate average is 0.054.

Health Electroencephalogram epileptic classification based on Hilbert probability similarity

“…Changing this outdated classification system proves the difficulty of the process which requires a lot of time and effort. Several studies in biomedical signal processing have focused on the development of classification systems for automatic analysis [5]. Moreover, they concentrated on EEG signals in the detection of epileptic seizures and the identification of eye states EEG [6].…”