A Hybrid Deep Learning Approach for Epileptic Seizure Detection in EEG Signals

Journal article


Ahmad, I., Wang, X., Javeed, D., Kumar, P., Samuel, O.W. and Chen, S. 2023. A Hybrid Deep Learning Approach for Epileptic Seizure Detection in EEG Signals. IEEE Journal of Biomedical and Health Informatics. https://doi.org/10.1109/JBHI.2023.3265983
AuthorsAhmad, I., Wang, X., Javeed, D., Kumar, P., Samuel, O.W. and Chen, S.
Abstract

Early detection and proper treatment of epilepsy is essential and meaningful to those who suffer from this disease. The adoption of deep learning (DL) techniques for automated epileptic seizure detection using electroencephalography (EEG) signals has shown great potential in making the most appropriate and fast medical decisions. However, DL algorithms have high computational complexity and suffer low accuracy with imbalanced medical data in multi seizure-classification task. Motivated from the aforementioned challenges, we present a simple and effective hybrid DL approach for epileptic seizure detection in EEG signals. Specifically, first we use a K-means Synthetic minority oversampling technique (SMOTE) to balance the sampling data. Second, we integrate a 1D Convolutional Neural Network (CNN) with a Bidirectional Long Short-Term Memory (BiLSTM) network based on Truncated Backpropagation Through Time (TBPTT) to efficiently extract spatial and temporal sequence information while reducing computational complexity. Finally, the proposed DL architecture uses softmax and sigmoid classifiers at the classification layer to perform multi and binary seizure-classification tasks. In addition, the 10-fold cross-validation technique is performed to show the significance of the proposed DL approach. Experimental results using the publicly available UCI epileptic seizure recognition data set shows better performance in terms of precision, sensitivity, specificity, and F1-score over some baseline DL algorithms and recent state-of-the-art techniques.

KeywordsConvolutional Neural Network; Epileptic Seizure; Electroencephalography (EEG)
Year2023
JournalIEEE Journal of Biomedical and Health Informatics
PublisherIEEE
ISSN2168-2208
Digital Object Identifier (DOI)https://doi.org/10.1109/JBHI.2023.3265983
Web address (URL)https://ieeexplore.ieee.org/document/10098564
Accepted author manuscript
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Open
Output statusPublished
Publication dates
Online10 Apr 2023
Publication process dates
Accepted01 Apr 2023
Deposited22 Jun 2023
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https://repository.derby.ac.uk/item/9z295/a-hybrid-deep-learning-approach-for-epileptic-seizure-detection-in-eeg-signals

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