Epileptic EEG signal classification using convolutional neural networks based on optimum window length and FFT's length

Santoso, Irwan Budi and Purnama, I Ketut Eddy (2020) Epileptic EEG signal classification using convolutional neural networks based on optimum window length and FFT's length. Presented at ICCCM'20: 2020 The 8th International Conference on Computer and Communications Management, Jul 2020, Singapore.

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Abstract

This paper presents the method for the epilepsy classification based on electroencephalogram (EEG) signals. This method uses the spectrogram image of EEG signals and convolutional neural networks (CNN).The spectrogram image is obtained by mapping the spectrogram value of the results of the short-time Fourier Transform (STFT) to the RGB color map. The best spectrogram for CNN is determined based on the length of fast Fourier transform (FFT) and window length in the windowing technique. The proposed method is evaluated with the epileptic EEG signal datasets. The experimental results show CNN works optimally with spectrogram images from STFT on the window length of 128 and the length of FFT of 128. In these conditions, the performance of CNN in the classification of epilepsy is better than others and able to compete with existing methods.

Item Type:	Conference (Paper)
Subjects:	08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080199 Artificial Intelligence and Image Processing not elsewhere classified
Divisions:	Faculty of Technology > Department of Informatics Engineering
Depositing User:	irwan budi santoso
Date Deposited:	04 Jun 2021 08:15

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