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

Irwan Budi Santoso, I. Ketut Eddy Purnama

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings of the 8th International Conference on Computer and Communications Management, ICCCM 2020
PublisherAssociation for Computing Machinery
Pages87-91
Number of pages5
ISBN (Electronic)9781450387668
DOIs
Publication statusPublished - 17 Jul 2020
Event8th International Conference on Computer and Communications Management, ICCCM 2020 - Virtual, Online, Singapore
Duration: 17 Jul 202019 Jul 2020

Publication series

NameACM International Conference Proceeding Series

Conference

Conference8th International Conference on Computer and Communications Management, ICCCM 2020
Country/TerritorySingapore
CityVirtual, Online
Period17/07/2019/07/20

Keywords

  • Signal
  • convolutional neural networks
  • length of FFT
  • short-time Fourier Transform
  • spectrogram image
  • window length

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