Epileptic EEG Signal Classification Using Convolutional Neural Network Based on Multi-Segment of EEG Signal

Irwan Budi Santoso, Yudhi Adrianto, Anggraini Dwi Sensusiati, Diah Puspito Wulandari, I. Ketut Eddy Purnama

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)

Abstract

High performance in the epileptic electroencephalogram (EEG) signal classification is an important step in diagnosing epilepsy. Furthermore, this classification is carried out to determine whether the EEG signal from a person's examination results is categorized as an epileptic signal or not (healthy). Several automated techniques have been proposed to assist neurologists in classifying these signals. In general, these techniques have yielded a high average accuracy in classification, but the performance still needs to be improved. Therefore, we propose a convolutional neural network based on multi-segment of EEG signals to classify epileptic EEG signals. This method is built to overcome data limitations in the convolutional neural network training process and add the ensemble combination process. The multi-segment of EEG signal is formed by splitting the signal without overlapping each channel and converting it into the spectrogram image based on the short-time Fourier transform value. The spectrogram image is then used as input for the convolutional neural network in in-depth training and testing. The convolutional neural network model of the training results is used to classify each EEG signal segment on each test channel before entering the ensemble combination stage for the final classification. To evaluate the performance of our proposed method, we used the Bonn EEG dataset. The dataset consists of five EEG records labelled as A, B, C, D, and E. The experiments on several datasets (AB-C, AB-D, AB-E, AB-CD, AB-CDE, and AB-CD-E) which were arranged from the dataset showed that our proposed method (with segment) performs better than without segment. Our proposed method yielded the best average of classification accuracy which is 99.33%, 100%, 100%, 99.5%, 99.8%, and 99.4% for the AB-C, AB-D, AB-E, AB-CD, AB-CDE, and AB-CD-E. By these results, the proposed method can outperform several other methods on the same dataset.

Original languageEnglish
Pages (from-to)160-176
Number of pages17
JournalInternational Journal of Intelligent Engineering and Systems
Volume14
Issue number3
DOIs
Publication statusPublished - 2021

Keywords

  • Convolutional neural network
  • Electroencephalogram
  • Ensemble combination
  • Segment
  • Short time fourier transform
  • Spectrogram image

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