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
License
All rights reserved
File Access Level
Open
Output statusPublished
Publication dates
Online10 Apr 2023
Publication process dates
Accepted01 Apr 2023
Deposited22 Jun 2023
Supplemental file
File Access Level
Controlled
Permalink -

https://repository.derby.ac.uk/item/9z295/a-hybrid-deep-learning-approach-for-epileptic-seizure-detection-in-eeg-signals

Download files


Accepted author manuscript
  • 77
    total views
  • 59
    total downloads
  • 0
    views this month
  • 0
    downloads this month

Export as

Related outputs

Robust Epileptic Seizure Detection Based on Biomedical Signals Using an Advanced Multi-View Deep Feature Learning Approach
Ahmad, I., Liu, Z., Li, L., Ullah, I., Wang, X., Samuel, O., Li, G., Tao, Y., Chen, Y. and Chen, S. 2024. Robust Epileptic Seizure Detection Based on Biomedical Signals Using an Advanced Multi-View Deep Feature Learning Approach. IEEE Journal of Biomedical and Health Informatics. pp. 1-13. https://doi.org/10.1109/JBHI.2024.3396130
A robust feature adaptation approach against variation of muscle contraction forces for myoelectric pattern recognition-based gesture characterization
Samuel, O., Asogbon, M. and McEwan, A. 2024. A robust feature adaptation approach against variation of muscle contraction forces for myoelectric pattern recognition-based gesture characterization. Biomedical Signal Processing and Control. 95 (2024), p. 106446. https://doi.org/https://doi.org/10.1016/j.bspc.2024.106446
Exploring EEG Signals for Noninvasive Blood Glucose Monitoring in Prediabetes Diagnosis
Igbe, T., Kandwal, A., Li, J., Kulwa, F., Samuel, O. and Nie, Z. 2024. Exploring EEG Signals for Noninvasive Blood Glucose Monitoring in Prediabetes Diagnosis. IEEE Transactions on Instrumentation and Measurements. 73, pp. 1-8. https://doi.org/10.1109/TIM.2024.3400333
A Pilot on the use of Stride Cadence for the Charac-terization of Walking Ability in Lower Limb Ampu-tees
Nsugbe, E., Samuel, O., Asogbon, M. and Jose, J. R. L. 2024. A Pilot on the use of Stride Cadence for the Charac-terization of Walking Ability in Lower Limb Ampu-tees. Biomedical Engineering Advances. 7 (2024), pp. 1-10. https://doi.org/10.1016/j.bea.2024.100117
Investigation of Artifact Contamination Impact on EEG Oscillations Towards Enhanced Motor Function Characterization
Asogbon, M.G., Samuel, O., Meziane, F., Li, G. and Li, Y. 2024. Investigation of Artifact Contamination Impact on EEG Oscillations Towards Enhanced Motor Function Characterization. 17th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOSIGNALS. SCITEPRESS - Science and Technology Publications. https://doi.org/10.5220/0012373400003657
Continuous Kalman Estimation Method for Finger Kinematics Tracking from Surface Electromyography
Zhang, H., Peng, B., Tian, L., Samuel, O. and Li, G. 2024. Continuous Kalman Estimation Method for Finger Kinematics Tracking from Surface Electromyography. Cyborg and Bionic Systems. pp. 1-11. https://doi.org/10.34133/cbsystems.0094
An Efficient Feature Selection and Explainable Classification Method for EEG-based Epileptic Seizure Detection
Ahmad, I., Yao, C., Li, L., Chen, Y., Liu, Z., Ullah, I., Shabaz, M., Wang, X., Huang, K., Li, G., Zhao, G., Samuel, O. and Chen, S. 2023. An Efficient Feature Selection and Explainable Classification Method for EEG-based Epileptic Seizure Detection. Journal of Information Security and Applications. 80, pp. 1-17. https://doi.org/10.1016/j.jisa.2023.103654
Towards Adequate Policy Enhancement: An AI-Driven Decision Tree Model for Efficient Recognition and Classification of EPA Status via Multi-Emission Parameters
Awomuti, A., Alimo, P., Young, G., Agyeman, S., Akintunde, T., Agbeja, A., Oderinde, O., Samuel, O. and Otobrise, H. 2023. Towards Adequate Policy Enhancement: An AI-Driven Decision Tree Model for Efficient Recognition and Classification of EPA Status via Multi-Emission Parameters. City and Environment Interactions. 20, pp. 1-12. https://doi.org/10.1016/j.cacint.2023.100127
A Multi-Dataset Characterization of Window-based Hyperparameters for Deep CNN-driven sEMG Pattern Recognition
Kulwa, F., Zhang, H., Samuel, O., Asogbon, M., Scheme, E., Kushaba, R., McEwan, A. and Li, G. 2023. A Multi-Dataset Characterization of Window-based Hyperparameters for Deep CNN-driven sEMG Pattern Recognition. IEEE Transactions on Human-Machine Systems. pp. 1-12. https://doi.org/10.1109/THMS.2023.3329536
Analysis of Artifactual Components Rejection Threshold towards Enhanced Characterization of Neural Activity in Post-Stroke Survivor
Asogbon, M., Huai, Y., Samuel, O., Jing, Z., Ma, Y., Liu, J., Jiang, Y., Fu, Y., Li, G. and Li, Y. 2023. Analysis of Artifactual Components Rejection Threshold towards Enhanced Characterization of Neural Activity in Post-Stroke Survivor. 45th Annual International Conference of the IEEE, Engineering in Medicine and Biology Society. IEEE Xplore. https://doi.org/https://doi.org/10.1109/EMBC40787.2023.10340688
An Attention-based Bidirectional LSTM Model for Continuous Cross-subject Estimation of Knee Joint Angle during Running from sEMG Signals
Zangene, A., Samuel, O., Abbasi, A., Nazarpour, K., McEwan, A. and Li, G. 2023. An Attention-based Bidirectional LSTM Model for Continuous Cross-subject Estimation of Knee Joint Angle during Running from sEMG Signals. 45th Annual International Conference of the IEEE, Engineering in Medicine and Biology Society. IEEE. https://doi.org/10.1109/EMBC40787.2023.10340791
Enhanced Deep Transfer Learning Model based on Spatial-Temporal driven Scalograms for Precise Decoding of Motor Intent in Stroke Survivors
Samuel, O., Asogbon, M., Kulwa, F., Zangene, A., Oyemakinde, T., Igbe, T., McEwan, A., Li, Y. and Li, G. 2023. Enhanced Deep Transfer Learning Model based on Spatial-Temporal driven Scalograms for Precise Decoding of Motor Intent in Stroke Survivors. 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE. https://doi.org/10.1109/EMBC40787.2023.10340683
Inspection of EEG Signals for Noninvasive Blood Glucose Monitoring in Prediabetes Diagnosis
Igbe, T., Samuel, O.W., Li, J., Kulwa, F., Kandwal, A. and Nie, Z. 2023. Inspection of EEG Signals for Noninvasive Blood Glucose Monitoring in Prediabetes Diagnosis. IEEE International Workshop on Medical Measurement and Applications (MEMEA). IEEE. https://doi.org/10.1109/MeMeA57477.2023.10171941
A Novel Duo-Stage driven Deep Neural Network Approach for Mitigating Electrode Shift Impact on Myoelectric Pattern Recognition Systems
Kulwa, F., Samuel, O.W., Asogbon, M., Oyemakinde, T.T., Obe, O.O. and Li, G. 2023. A Novel Duo-Stage driven Deep Neural Network Approach for Mitigating Electrode Shift Impact on Myoelectric Pattern Recognition Systems. 2023 IEEE International Symposium on Medical Measurements and Applications (MeMeA). IEEE. https://doi.org/10.1109/MeMeA57477.2023.10171910
An efficient attention-driven deep neural network approach for continuous estimation of knee joint kinematics via sEMG signals during running
Zangene, A. R., Samuel, O., Abbasi, A., McEwan, A., Asogbon, M. G., Li, G. and Nazarpour, K. 2023. An efficient attention-driven deep neural network approach for continuous estimation of knee joint kinematics via sEMG signals during running. Biomedical Signal Processing and Control. 86 (B), pp. 1-12. https://doi.org/10.1016/j.bspc.2023.105103
A Hybrid Strategy-based Ultra-narrow Stretchable Microelectrodes with Cell-level Resolution
Li, F., Han, F., Wang, L., Huang, L., Samuel, O.W., Zhao, H., Xie, R., Wang, P., Tian, Q., Li, Q., Zhao, Y., Yu, Mei, Sun, J., Yang, R., Zhou, X., Li, F., Li, G., Lu, Y., Guo, P. and Liu, Z. 2023. A Hybrid Strategy-based Ultra-narrow Stretchable Microelectrodes with Cell-level Resolution. Advanced Functional Materials. 2300859, pp. 1-9. https://doi.org/10.1002/adfm.202300859
On the prediction of premature births in Hispanic labour patients using uterine contractions, heart beat signals and prediction machines
Nsugbe, E., Reyes-Lagos, J.J., Adams, D. and Samuel, O. 2023. On the prediction of premature births in Hispanic labour patients using uterine contractions, heart beat signals and prediction machines. Healthcare Technology Letters. 10 (1-2), pp. 11-22. https://doi.org/10.1049/htl2.12044
Surface Electromyogram, Kinematic, and Kinetic Dataset of Lower Limb Walking for Movement Intent Recognition
Wei, W., Tan, F., Zhang, H., Mao, H., Fu, M., Samuel, O.W. and Li, G. 2023. Surface Electromyogram, Kinematic, and Kinetic Dataset of Lower Limb Walking for Movement Intent Recognition. Nature Scientific Data. 10 (358), pp. 1-16. https://doi.org/10.1038/s41597-023-02263-3