A Hybrid Deep Learning Approach for Epileptic Seizure Detection in EEG Signals
Journal article
Authors | Ahmad, I., Wang, X., Javeed, D., Kumar, P., Samuel, O.W. and Chen, S. |
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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. |
Keywords | Convolutional Neural Network; Epileptic Seizure; Electroencephalography (EEG) |
Year | 2023 |
Journal | IEEE Journal of Biomedical and Health Informatics |
Publisher | IEEE |
ISSN | 2168-2208 |
Digital Object Identifier (DOI) | https://doi.org/10.1109/JBHI.2023.3265983 |
Web address (URL) | https://ieeexplore.ieee.org/document/10098564 |
Accepted author manuscript | License All rights reserved File Access Level Open |
Output status | Published |
Publication dates | |
Online | 10 Apr 2023 |
Publication process dates | |
Accepted | 01 Apr 2023 |
Deposited | 22 Jun 2023 |
Supplemental file | File Access Level Controlled |
https://repository.derby.ac.uk/item/9z295/a-hybrid-deep-learning-approach-for-epileptic-seizure-detection-in-eeg-signals
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Accepted author manuscript
2023_IEEE_JBHI_Accepted Manuscript-A Hybrid Deep Learning for Epileptic Seizure Detection.pdf | ||
License: All rights reserved | ||
File access level: Open |
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