Automatic epileptic seizure detection algorithm based on dual density dual tree complex wavelet transform_Journal of Biomedical Engineering

Authors：

KANG Tongzhou ¹ , ZUO Rundong ¹ , ZHONG Lanfeng ¹ , CHEN Wenjing ² ,  ZHANG Heng ² , LIU Hongxiu ³ ,  LAI Dakun ¹

1. School of Electronic Science and Engineering, University of Electronic Science and Technology, Chengdu 610054, P.R.China;
2. West China Hospital, Sichuan University, Chengdu 610041, P.R.China;
3. 29th Research Institute of CETC, Chengdu 610093, P.R.China;

Corresponding author：

ZHANG Heng, Email: Ohwhat@163.com; LAI Dakun, Email: dklai@uestc.edu.cn

Keywords：

epilepsy; intracranial electroencephalogram; dual density dual tree complex wavelet; wavelet entropy; epileptic seizure detection

DOI：

10.7507/1001-5515.202105075

Video：

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Abstract Full text Figures/Tables Video References Cited by

It is very important for epilepsy treatment to distinguish epileptic seizure and non-seizure. In this study, an automatic seizure detection algorithm based on dual density dual tree complex wavelet transform (DD-DT CWT) for intracranial electroencephalogram (iEEG) was proposed. The experimental data were collected from 15 719 competition data set up by the National Institutes of Health (NINDS) in Kaggle. The processed database consisted of 55 023 seizure epochs and 501 990 non-seizure epochs. Each epoch was 1 second long and contained 174 sampling points. Firstly, the signal was resampled. Then, DD-DT CWT was used for EEG signal processing. Four kinds of features include wavelet entropy, variance, energy and mean value were extracted from the signal. Finally, these features were sent to least squares-support vector machine (LS-SVM) for learning and classification. The appropriate decomposition level was selected by comparing the experimental results under different wavelet decomposition levels. The experimental results showed that the features selected in this paper were different between seizure and non-seizure. Among the eight patients, the average accuracy of three-level decomposition classification was 91.98%, the sensitivity was 90.15%, and the specificity was 93.81%. The work of this paper shows that our algorithm has excellent performance in the two classification of EEG signals of epileptic patients, and can detect the seizure period automatically and efficiently.

Citation： KANG Tongzhou, ZUO Rundong, ZHONG Lanfeng, CHEN Wenjing, ZHANG Heng, LIU Hongxiu, LAI Dakun. Automatic epileptic seizure detection algorithm based on dual density dual tree complex wavelet transform. Journal of Biomedical Engineering, 2021, 38(6): 1035-1042. doi: 10.7507/1001-5515.202105075 Copy

1.	Löscher W, Potschka H, Sisodiya S M, et al. Drug resistance in epilepsy: clinical impact, potential mechanisms, and new innovative treatment options. Pharmacol Rev, 2020, 72(3): 606-638.
2.	Johnson E, Kam J, Tzovara A, et al. Insights into human cognition from intracranial EEG: A review of audition, memory, internal cognition, and causality. J Neural Eng, 2020, 17(5): 051001.
3.	Rahul J, Sora M, Sharma L D. An overview on biomedical signal analysis. Int J Recent Technol Eng, 2019, 7: 206-209.
4.	Kingsbury N. A dual-tree complex wavelet transform with improved orthogonality and symmetry properties// Proceedings 2000 International Conference on Image Processing. Canada: IEEE, 2000, 2: 375-378.
5.	Selesnick I W, Abdelnour A F. Symmetric wavelet tight frames with two generators. Appl Comput Harmon A, 2004, 17(2): 211-225.
6.	Selesnick I W. The double-density dual-tree DWT. IEEE Signal Proces, 2004, 52(5): 1304-1314.
7.	Yang H, Guan C, Chua K S G, et al. Detection of motor imagery of swallow EEG signals based on the dual-tree complex wavelet transform and adaptive model selection. J Neural Eng, 2014, 11(3): 035016.
8.	Roy V, Shukla S. A NLMS based approach for artifacts removal in multichannel EEG signals with ICA and double density wavelet transform// 2015 Fifth International Conference on Communication Systems and Network Technologies. Gwalior: IEEE, 2015: 461-466.
9.	范虹, 张程程, 侯存存, 等. 结合双树复小波变换和改进密度峰值快速搜索聚类的乳腺MR图像分割. 电子学报, 2019, 47(10): 2149-2157.
10.	王高峰, 石钟磊, 谭魏盟, 等. 融合双密度双树复小波变换与多尺度Retinex的图像增强算法. 计算机与数字工程, 2017, 45(5): 870-873, 934.
11.	Wang L, Liu Z, Cao H, et al. Subband averaging kurtogram with dual-tree complex wavelet packet transform for rotating machinery fault diagnosis. Mech Syst Signal PR, 2020, 142: 106755.
12.	魏书会, 刘翠响, 王宝珠, 等. 融合双密度双树小波与LLTSA的人脸识别算法. 电子设计工程, 2015, 23(17): 179-181.
13.	Suykens J A K, Vandewalle J. Training multilayer perceptron classifiers based on a modified support vector method. IEEE T Neural Networ, 1999, 10(4): 907-911.
14.	Sharma R, Pachori R B, Acharya U R. Application of entropy measures on intrinsic mode functions for the automated identification of focal electroencephalogram signals. Entropy, 2015, 17(2): 669-691.
15.	Liu H, Liu G H, Shen Y. Lifting wavelet scheme and wavelet energy entropy theory for transient power quality detection// 2008 7th World Congress on Intelligent Control and Automation. Chongqing: IEEE, 2008: 6011-6016.
16.	Natwong B, Sooraksa P, Pintavirooj C, et al. Wavelet entropy analysis of the high resolution ECG// 2006 1st IEEE Conference on Industrial Electronics and Applications. Singapore: IEEE, 2006: 1-4.
17.	Deivasigamani S, Senthilpari C, Yong W H. Classification of focal and nonfocal EEG signals using ANFIS classifier for epilepsy detection. Int J Imag Syst Tech, 2016, 26(4): 277-283.
18.	Xiang J, Li C, Li H, et al. The detection of epileptic seizure signals based on fuzzy entropy. J Neurosci Meth, 2015, 243: 18-25.
19.	Bhattacharyya A, Pachori R B, Acharya U R. Tunable-Q wavelet transform based multivariate sub-band fuzzy entropy with application to focal EEG signal analysis. Entropy, 2017, 19(3): 99.
20.	Parhi K K. Teaching digital signal processing by partial flipping, active learning, and visualization: Keeping students engaged with blended teaching. IEEE Signal Proc Mag, 2021, 38(3): 20-29.
21.	Zhou M, Tian C, Cao R, et al. Epileptic seizure detection based on EEG signals and CNN. Front Neuroinform, 2018, 12: 95.
22.	Cho D, Min B, Kim J, et al. EEG-based prediction of epileptic seizures using phase synchronization elicited from noise-assisted multivariate empirical mode decomposition. IEEE T Neur Sys Reh, 2016, 25(8): 1309-1318.
23.	Ciurea A, Manoila C P, Tautan A M, et al. Low latency automated epileptic seizure detection: Individualized vs. global approaches// 2020 International Conference on e-Health and Bioengineering (EHB). Grigore: IEEE, 2020: 1-4.
24.	Emami A, Kunii N, Matsuo T, et al. Seizure detection by convolutional neural network-based analysis of scalp electroencephalography plot images. Neuroimage-Clin, 2019, 22: 101684.
25.	Acharya U R, Hagiwara Y, Deshpande S N, et al. Characterization of focal EEG signals: a review. Future Gener Comp SY, 2019, 91: 290-299.
26.	Das A B, Bhuiyan M I H. Discrimination and classification of focal and non-focal EEG signals using entropy-based features in the EMD-DWT domain. Boimed Signal Proces, 2016, 29: 11-21.

1. Löscher W, Potschka H, Sisodiya S M, et al. Drug resistance in epilepsy: clinical impact, potential mechanisms, and new innovative treatment options. Pharmacol Rev, 2020, 72(3): 606-638.
2. Johnson E, Kam J, Tzovara A, et al. Insights into human cognition from intracranial EEG: A review of audition, memory, internal cognition, and causality. J Neural Eng, 2020, 17(5): 051001.
3. Rahul J, Sora M, Sharma L D. An overview on biomedical signal analysis. Int J Recent Technol Eng, 2019, 7: 206-209.
4. Kingsbury N. A dual-tree complex wavelet transform with improved orthogonality and symmetry properties// Proceedings 2000 International Conference on Image Processing. Canada: IEEE, 2000, 2: 375-378.
5. Selesnick I W, Abdelnour A F. Symmetric wavelet tight frames with two generators. Appl Comput Harmon A, 2004, 17(2): 211-225.
6. Selesnick I W. The double-density dual-tree DWT. IEEE Signal Proces, 2004, 52(5): 1304-1314.
7. Yang H, Guan C, Chua K S G, et al. Detection of motor imagery of swallow EEG signals based on the dual-tree complex wavelet transform and adaptive model selection. J Neural Eng, 2014, 11(3): 035016.
8. Roy V, Shukla S. A NLMS based approach for artifacts removal in multichannel EEG signals with ICA and double density wavelet transform// 2015 Fifth International Conference on Communication Systems and Network Technologies. Gwalior: IEEE, 2015: 461-466.
9. 范虹, 张程程, 侯存存, 等. 结合双树复小波变换和改进密度峰值快速搜索聚类的乳腺MR图像分割. 电子学报, 2019, 47(10): 2149-2157.
10. 王高峰, 石钟磊, 谭魏盟, 等. 融合双密度双树复小波变换与多尺度Retinex的图像增强算法. 计算机与数字工程, 2017, 45(5): 870-873, 934.
11. Wang L, Liu Z, Cao H, et al. Subband averaging kurtogram with dual-tree complex wavelet packet transform for rotating machinery fault diagnosis. Mech Syst Signal PR, 2020, 142: 106755.
12. 魏书会, 刘翠响, 王宝珠, 等. 融合双密度双树小波与LLTSA的人脸识别算法. 电子设计工程, 2015, 23(17): 179-181.
13. Suykens J A K, Vandewalle J. Training multilayer perceptron classifiers based on a modified support vector method. IEEE T Neural Networ, 1999, 10(4): 907-911.
14. Sharma R, Pachori R B, Acharya U R. Application of entropy measures on intrinsic mode functions for the automated identification of focal electroencephalogram signals. Entropy, 2015, 17(2): 669-691.
15. Liu H, Liu G H, Shen Y. Lifting wavelet scheme and wavelet energy entropy theory for transient power quality detection// 2008 7th World Congress on Intelligent Control and Automation. Chongqing: IEEE, 2008: 6011-6016.
16. Natwong B, Sooraksa P, Pintavirooj C, et al. Wavelet entropy analysis of the high resolution ECG// 2006 1st IEEE Conference on Industrial Electronics and Applications. Singapore: IEEE, 2006: 1-4.
17. Deivasigamani S, Senthilpari C, Yong W H. Classification of focal and nonfocal EEG signals using ANFIS classifier for epilepsy detection. Int J Imag Syst Tech, 2016, 26(4): 277-283.
18. Xiang J, Li C, Li H, et al. The detection of epileptic seizure signals based on fuzzy entropy. J Neurosci Meth, 2015, 243: 18-25.
19. Bhattacharyya A, Pachori R B, Acharya U R. Tunable-Q wavelet transform based multivariate sub-band fuzzy entropy with application to focal EEG signal analysis. Entropy, 2017, 19(3): 99.
20. Parhi K K. Teaching digital signal processing by partial flipping, active learning, and visualization: Keeping students engaged with blended teaching. IEEE Signal Proc Mag, 2021, 38(3): 20-29.
21. Zhou M, Tian C, Cao R, et al. Epileptic seizure detection based on EEG signals and CNN. Front Neuroinform, 2018, 12: 95.
22. Cho D, Min B, Kim J, et al. EEG-based prediction of epileptic seizures using phase synchronization elicited from noise-assisted multivariate empirical mode decomposition. IEEE T Neur Sys Reh, 2016, 25(8): 1309-1318.
23. Ciurea A, Manoila C P, Tautan A M, et al. Low latency automated epileptic seizure detection: Individualized vs. global approaches// 2020 International Conference on e-Health and Bioengineering (EHB). Grigore: IEEE, 2020: 1-4.
24. Emami A, Kunii N, Matsuo T, et al. Seizure detection by convolutional neural network-based analysis of scalp electroencephalography plot images. Neuroimage-Clin, 2019, 22: 101684.
25. Acharya U R, Hagiwara Y, Deshpande S N, et al. Characterization of focal EEG signals: a review. Future Gener Comp SY, 2019, 91: 290-299.
26. Das A B, Bhuiyan M I H. Discrimination and classification of focal and non-focal EEG signals using entropy-based features in the EMD-DWT domain. Boimed Signal Proces, 2016, 29: 11-21.

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