Classification of Spike-wave Discharge with STFT Approach

Authors

  • N. Mustafa Faculty of Electrical & Electronics Engineering, Universiti Malaysia Pahang, Pekan Campus, 26600 Pekan, Pahang, Malaysia
  • N.A.A Lokman Faculty of Electrical & Electronics Engineering, Universiti Malaysia Pahang, Pekan Campus, 26600 Pekan, Pahang, Malaysia

Abstract


Spike-Wave Discharge (STD) is an abnormal brainwave pattern in the brain area that has possibility of generating an epilepsy seizure. The brainwave 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.

 

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Published

2014-06-30

How to Cite

Mustafa, N., & Lokman, N. (2014). Classification of Spike-wave Discharge with STFT Approach. Journal of Engineering and Technology (JET), 5(1), 11–20. Retrieved from https://jet.utem.edu.my/jet/article/view/123

Issue

Vol. 5 No. 1 (2014): JOURNAL OF ENGINEERING AND TECHNOLOGY

Section

Articles

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