Automatic epilepsy detection with an attention-based multiscale residual network_Journal of Biomedical Engineering

Authors：

WANG Xingqi ¹ ,  LI Ming’ai ^1,2,3

1. Faculty of Information Technology, Beijing University of Technology, Beijing 100124, P. R. China;
2. Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing 100124, P. R. China;
3. Engineering Research Center of Digital Community, Ministry of Education, Beijing 100124, P. R. China;

Corresponding author：

LI Ming’ai, Email: limingai@bjut.edu.cn

Keywords：

Electroencephalogram signal; Multi-scale principal component analysis (MSPCA); Brain networks; Deep learning; Seizure detection

DOI：

10.7507/1001-5515.202307030

Video：

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The deep learning-based automatic detection of epilepsy electroencephalogram (EEG), which can avoid the artificial influence, has attracted much attention, and its effectiveness mainly depends on the deep neural network model. In this paper, an attention-based multi-scale residual network (AMSRN) was proposed in consideration of the multiscale, spatio-temporal characteristics of epilepsy EEG and the information flow among channels, and it was combined with multiscale principal component analysis (MSPCA) to realize the automatic epilepsy detection. Firstly, MSPCA was used for noise reduction and feature enhancement of original epilepsy EEG. Then, we designed the structure and parameters of AMSRN. Among them, the attention module (AM), multiscale convolutional module (MCM), spatio-temporal feature extraction module (STFEM) and classification module (CM) were applied successively to signal reexpression with attention weighted mechanism as well as extraction, fusion and classification for multiscale and spatio-temporal features. Based on the Children’s Hospital Boston-Massachusetts Institute of Technology ( CHB-MIT) public dataset, the AMSRN model achieved good results in sensitivity (98.56%), F1 score (98.35%), accuracy (98.41%) and precision (98.43%). The results show that AMSRN can make good use of brain network information flow caused by seizures to enhance the difference among channels, and effectively capture the multiscale and spatio-temporal features of EEG to improve the performance of epilepsy detection.

Citation： WANG Xingqi, LI Ming’ai. Automatic epilepsy detection with an attention-based multiscale residual network. Journal of Biomedical Engineering, 2024, 41(2): 253-261. doi: 10.7507/1001-5515.202307030 Copy

1.	张瑞, 宋江玲, 胡文凤. 癫痫脑电的特征提取方法综述. 西北大学学报(自然科学版), 2016, 46(6): 781-788,794.
2.	Sharma R, Pachori R B. Classification of epileptic seizures in EEG signals based on phase space representation of intrinsic mode functions. Expert Syst Appl, 2015, 42(3): 1106-1117.
3.	Zabihi M, Kiranyaz S, Jäntti V, et al. Patient-specific seizure detection using nonlinear dynamics and nullclines. IEEE J Biomed Health Inform, 2019, 24(2): 543-555.
4.	Jaffino G, Jose J P, Kumar K S. Expectation-maximization extreme machine learning classifier for epileptic seizure detection// 2021 International Conference on Forensics, Analytics, Big Data, Security (FABS). Bengaluru: IEEE, 2021, 1: 1-5.
5.	徐嘉阳, 杨婷婷, 李雯, 等. 基于自适应多尺度脑功能连接的局灶性癫痫发作检测方法研究. 中国生物医学工程学报, 2022, 41(4): 393-401.
6.	Rajaguru H, Santhosh B. Logistic regression classifier with hybrid dimensionality reduction techniques for epilepsy detection from EEG signals// 2022 Smart Technologies, Communication and Robotics (STCR). Sathyamangalam: IEEE, 2022: 1-5.
7.	Ashokkumar S R, Premkumar M, Dhilipkumar P, et al. Application of multi-domain feature for automated seizure detection from EEG signal// 2022 3rd International Conference on Smart Electronics and Communication (ICOSEC). Trichy: IEEE, 2022: 280-285.
8.	Yuan S, Liu X, Shang J, et al. Automatic seizure detection using Logarithmic Euclidean-Gaussian Mixture Models (LE-GMMs) and improved Deep Forest learning. IEEE J Biomed Health Inform, 2022, 27(3): 1386-1396.
9.	Zubair M, Belykh M V, Naik M U K, et al. Detection of epileptic seizures from EEG signals by combining dimensionality reduction algorithms with machine learning models. IEEE Sens J, 2021, 21(15): 16861-16869.
10.	Hussain W, Wang B, Niu Y, et al. Epileptic seizure detection with permutation fuzzy entropy using robust machine learning techniques. IEEE Access, 2019, 7: 182238-182258.
11.	Yao X, Li X, Ye Q, et al. A robust deep learning approach for automatic classification of seizures against non-seizures. Biomed Signal Process Control, 2021, 64: 102215.
12.	Hu X, Yuan S, Xu F, et al. Scalp EEG classification using deep Bi-LSTM network for seizure detection. Comput Biol Med, 2020, 124: 103919.
13.	Shen M, Wen P, Song B, et al. Real-time epilepsy seizure detection based on EEG using tunable-Q wavelet transform and convolutional neural network. Biomed Signal Process Control, 2023, 82: 104566.
14.	Fukumori K, Yoshida N, Sugano H, et al. Epileptic spike detection by recurrent neural networks with self-attention mechanism// ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Singapore: IEEE, 2022: 1406-1410.
15.	Sharan R V, Berkovsky S. Epileptic seizure detection using multi-channel EEG wavelet power spectra and 1-D convolutional neural networks// 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). Montreal: IEEE, 2020: 545-548.
16.	Chatzichristos C, Dan J, Narayanan A M, et al. Epileptic seizure detection in EEG via fusion of multi-view attention-gated U-net deep neural networks// 2020 IEEE Signal Processing in Medicine and Biology Symposium (SPMB). Philadelphia: IEEE, 2020: 1-7.
17.	Ma Y, Huang Z, Su J, et al. A multi-channel feature fusion CNN-Bi-LSTM epilepsy EEG classification and prediction model based on attention mechanism. IEEE Access, 2023, 11: 62855-62864.
18.	Zhao Y, Chu D, He J, et al. Interactive local and global feature coupling for EEG-based epileptic seizure detection. Biomed Signal Process Control, 2023, 81: 104441.
19.	Tian X, Deng Z, Ying W, et al. Deep multi-view feature learning for EEG-based epileptic seizure detection. IEEE Trans Neural Syst Rehabilitation Eng, 2019, 27(10): 1962-1972.
20.	Ke N, Lin T, Lin Z. Channel-weighted squeeze-and-excitation networks for epileptic seizure detection// 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI). Washington: IEEE, 2021: 666-673.
21.	Li Y, Liu Y, Cui W G, et al. Epileptic seizure detection in EEG signals using a unified temporal-spectral squeeze-and-excitation network. IEEE Trans Neural Syst Rehabil Eng, 2020, 28(4): 782-794.
22.	He J, Cui J, Zhang G, et al. Spatial–temporal seizure detection with graph attention network and bi-directional LSTM architecture. Biomed Signal Process Control, 2022, 78: 103908.
23.	Poorani S, Balasubramanie P. Deep learning based epileptic seizure detection with EEG data. Int J Syst Assur Eng Manag, 2023: 1-10.
24.	Kevric J, Subasi A. The effect of multiscale PCA de-noising in epileptic seizure detection. J Med Syst, 2014, 38: 1-13.

1. 张瑞, 宋江玲, 胡文凤. 癫痫脑电的特征提取方法综述. 西北大学学报(自然科学版), 2016, 46(6): 781-788,794.
2. Sharma R, Pachori R B. Classification of epileptic seizures in EEG signals based on phase space representation of intrinsic mode functions. Expert Syst Appl, 2015, 42(3): 1106-1117.
3. Zabihi M, Kiranyaz S, Jäntti V, et al. Patient-specific seizure detection using nonlinear dynamics and nullclines. IEEE J Biomed Health Inform, 2019, 24(2): 543-555.
4. Jaffino G, Jose J P, Kumar K S. Expectation-maximization extreme machine learning classifier for epileptic seizure detection// 2021 International Conference on Forensics, Analytics, Big Data, Security (FABS). Bengaluru: IEEE, 2021, 1: 1-5.
5. 徐嘉阳, 杨婷婷, 李雯, 等. 基于自适应多尺度脑功能连接的局灶性癫痫发作检测方法研究. 中国生物医学工程学报, 2022, 41(4): 393-401.
6. Rajaguru H, Santhosh B. Logistic regression classifier with hybrid dimensionality reduction techniques for epilepsy detection from EEG signals// 2022 Smart Technologies, Communication and Robotics (STCR). Sathyamangalam: IEEE, 2022: 1-5.
7. Ashokkumar S R, Premkumar M, Dhilipkumar P, et al. Application of multi-domain feature for automated seizure detection from EEG signal// 2022 3rd International Conference on Smart Electronics and Communication (ICOSEC). Trichy: IEEE, 2022: 280-285.
8. Yuan S, Liu X, Shang J, et al. Automatic seizure detection using Logarithmic Euclidean-Gaussian Mixture Models (LE-GMMs) and improved Deep Forest learning. IEEE J Biomed Health Inform, 2022, 27(3): 1386-1396.
9. Zubair M, Belykh M V, Naik M U K, et al. Detection of epileptic seizures from EEG signals by combining dimensionality reduction algorithms with machine learning models. IEEE Sens J, 2021, 21(15): 16861-16869.
10. Hussain W, Wang B, Niu Y, et al. Epileptic seizure detection with permutation fuzzy entropy using robust machine learning techniques. IEEE Access, 2019, 7: 182238-182258.
11. Yao X, Li X, Ye Q, et al. A robust deep learning approach for automatic classification of seizures against non-seizures. Biomed Signal Process Control, 2021, 64: 102215.
12. Hu X, Yuan S, Xu F, et al. Scalp EEG classification using deep Bi-LSTM network for seizure detection. Comput Biol Med, 2020, 124: 103919.
13. Shen M, Wen P, Song B, et al. Real-time epilepsy seizure detection based on EEG using tunable-Q wavelet transform and convolutional neural network. Biomed Signal Process Control, 2023, 82: 104566.
14. Fukumori K, Yoshida N, Sugano H, et al. Epileptic spike detection by recurrent neural networks with self-attention mechanism// ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Singapore: IEEE, 2022: 1406-1410.
15. Sharan R V, Berkovsky S. Epileptic seizure detection using multi-channel EEG wavelet power spectra and 1-D convolutional neural networks// 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). Montreal: IEEE, 2020: 545-548.
16. Chatzichristos C, Dan J, Narayanan A M, et al. Epileptic seizure detection in EEG via fusion of multi-view attention-gated U-net deep neural networks// 2020 IEEE Signal Processing in Medicine and Biology Symposium (SPMB). Philadelphia: IEEE, 2020: 1-7.
17. Ma Y, Huang Z, Su J, et al. A multi-channel feature fusion CNN-Bi-LSTM epilepsy EEG classification and prediction model based on attention mechanism. IEEE Access, 2023, 11: 62855-62864.
18. Zhao Y, Chu D, He J, et al. Interactive local and global feature coupling for EEG-based epileptic seizure detection. Biomed Signal Process Control, 2023, 81: 104441.
19. Tian X, Deng Z, Ying W, et al. Deep multi-view feature learning for EEG-based epileptic seizure detection. IEEE Trans Neural Syst Rehabilitation Eng, 2019, 27(10): 1962-1972.
20. Ke N, Lin T, Lin Z. Channel-weighted squeeze-and-excitation networks for epileptic seizure detection// 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI). Washington: IEEE, 2021: 666-673.
21. Li Y, Liu Y, Cui W G, et al. Epileptic seizure detection in EEG signals using a unified temporal-spectral squeeze-and-excitation network. IEEE Trans Neural Syst Rehabil Eng, 2020, 28(4): 782-794.
22. He J, Cui J, Zhang G, et al. Spatial–temporal seizure detection with graph attention network and bi-directional LSTM architecture. Biomed Signal Process Control, 2022, 78: 103908.
23. Poorani S, Balasubramanie P. Deep learning based epileptic seizure detection with EEG data. Int J Syst Assur Eng Manag, 2023: 1-10.
24. Kevric J, Subasi A. The effect of multiscale PCA de-noising in epileptic seizure detection. J Med Syst, 2014, 38: 1-13.

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