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

Akeel Abdulkareem Alsakaa, Mohsin Hasan Hussein, Zaid Hasan Nasralla, Hazim Alsaqaa, Kesra Nermend, Anna Borawska


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.


Discrete wavelet transformation; Effective electroencephalogram; Epilepsy; Seizure detection; Statistical moments; Support vector machine

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DOI: http://doi.org/10.11591/ijece.v12i5.pp5204-5213

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International Journal of Electrical and Computer Engineering (IJECE)
p-ISSN 2088-8708, e-ISSN 2722-2578

This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).