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Detection of epileptic seizure using EEG signals analysis based on deep learning techniques

Author

Listed:
  • Abdulwahhab, Ali H.
  • Abdulaal, Alaa Hussein
  • Thary Al-Ghrairi, Assad H.
  • Mohammed, Ali Abdulwahhab
  • Valizadeh, Morteza

Abstract

The brain neurons' electrical activities represented by Electroencephalogram (EEG) signals are the most common data for diagnosing Epilepsy seizure, which is considered a chronic nervous disorder that cannot be controlled medically using surgical operation or medications with more than 40 % of Epilepsy seizure case. With the progress and development of artificial intelligence and deep learning techniques, it becomes possible to detect these seizures over the observation of the non-stationary-dynamic EEG signals, which contain important information about the mental state of patients. This paper provides a concerted deep machine learning model consisting of two simultaneous techniques detecting the activity of epileptic seizures using EEG signals. The time-frequency image of EEG waves and EEG raw waves are used as input components for the convolution neural network (CNN) and recurrent neural network (RNN) with long- and short-term memory (LSTM). Two processing signal methods have been used, Short-Time Fourier Transform (STFT) and Continuous Wavelet Transformation (CWT), have been used for generating spectrogram and scalogram images with sizes of 77 × 75 and 32 × 32, respectively. The experimental results showed a detection accuracy of 99.57 %, 99.57 % using CWT Scalograms, and 99.26 %, 97.12 % using STFT spectrograms as CNN input for the Bonn University dataset and the CHB-MIT dataset, respectively. Thus, the proposed models provide the ability to detect epileptic seizures with high success compared to previous studies.

Suggested Citation

  • Abdulwahhab, Ali H. & Abdulaal, Alaa Hussein & Thary Al-Ghrairi, Assad H. & Mohammed, Ali Abdulwahhab & Valizadeh, Morteza, 2024. "Detection of epileptic seizure using EEG signals analysis based on deep learning techniques," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
    • Handle: RePEc:eee:chsofr:v:181:y:2024:i:c:s0960077924002522
    DOI: 10.1016/j.chaos.2024.114700
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    References listed on IDEAS

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    1. Christian Janiesch & Patrick Zschech & Kai Heinrich, 2021. "Machine learning and deep learning," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(3), pages 685-695, September.
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