Classification of spike-wave discharge with STFT approach. Journal of Engineering and Technology, 5 (1). pp. 11-20. ISSN 2180-3811 (2014)
Abstract
Spike-Wave Discharge (STD) is an abnormal brainwave pattern in the brain area that has possibility of generating an epilepsy seizure. The brainwaves can be recorded by using Electroencephalogram (EEG) device. The purpose of this paper is to classify STD that occurred in epilepsy patient using k-Nearest Neighbor (kNN) with Short-Time Fourier Transform (STFT) approach. The EEG signals were downloaded from an established website that consisted of epilepsy and non-epilepsy samples. The process of artifact removal was done to ensure that the generated EEG signals and STFT were clean. Then, energy is extracted from STFT for four bands, namely Delta-band, Theta-band, Alpha-band and Beta-band. The experimental result showed that the kNN was able to classify the STD waves with 100% accuracy for the tested ratio training of 80:20.
| Item Type: | Article |
|---|---|
| Keywords: | Spike-Wave discharge, Epilepsy, Brainwave, EEG signals, STFT, kNN |
| Taxonomy: | By Subject > College of Engineering > Electrical Engineering > Electronics By Subject > College of Engineering > Electrical Engineering > Systems |
| Local Content Hub: | Subjects > College of Engineering |
| Depositing User: | Eza Eliana Abdul Wahid |
| Date Deposited: | 20 Jul 2022 02:21 |
| Last Modified: | 20 Jul 2022 02:21 |
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