Analysis of neural fragility in epileptic zone based on stereoelectroencephalography_Journal of Biomedical Engineering

Authors：

 YIN Ning ^1,2,3 , JIA Zhepei ^1,2,3 , WANG Le ⁴ , DONG Yilin ^1,2,3

1. State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, P. R. China;
2. Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300130, P. R. China;
3. Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300130, P. R. China;
4. Department of Neurology, Huanhu Hospital of Tianjin, Tianjin 300350, P. R. China;

Corresponding author：

YIN Ning, Email: yinning@hebut.edu.cn

Keywords：

Epilepsy; Neural fragility; Time-frequency analysis; Random forest

DOI：

10.7507/1001-5515.202211056

Video：

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There are some limitations in the localization of epileptogenic zone commonly used by human eyes to identify abnormal discharges of intracranial electroencephalography in epilepsy. However, at present, the accuracy of the localization of epileptogenic zone by extracting intracranial electroencephalography features needs to be further improved. As a new method using dynamic network model, neural fragility has potential application value in the localization of epileptogenic zone. In this paper, the neural fragility analysis method was used to analyze the stereoelectroencephalography signals of 35 seizures in 20 patients, and then the epileptogenic zone electrodes were classified using the random forest model, and the classification results were compared with the time-frequency characteristics of six different frequency bands extracted by short-time Fourier transform. The results showed that the area under curve (AUC) of epileptic focus electrodes based on time-frequency analysis was 0.870 (delta) to 0.956 (high gamma), and its classification accuracy increased with the increase of frequency band, while the AUC by using neural fragility could reach 0.957. After fusing the neural fragility and the time-frequency characteristics of the γ and high γ band, the AUC could be further increased to 0.969, which was improved on the original basis. This paper verifies the effectiveness of neural fragility in identifying epileptogenic zone, and provides a theoretical reference for its further clinical application.

Citation： YIN Ning, JIA Zhepei, WANG Le, DONG Yilin. Analysis of neural fragility in epileptic zone based on stereoelectroencephalography. Journal of Biomedical Engineering, 2023, 40(5): 837-842. doi: 10.7507/1001-5515.202211056 Copy

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2.	Beghi M, Cornaggia C M, Frigeni B, et al. Learning disorders in epilepsy. Epilepsia, 2010, 47(2): 14-18.
3.	熊敏, 苏化庆, 向明钧. 癫痫发病机制的研究进展. 中国当代医药, 2019, 26(30): 24-27.
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5.	Piao Y S, Lu D H, Chen L, et al. Neuropathological findings in intractable epilepsy: 435 Chinese cases. Brain Pathol, 2010, 20(5): 902-908.
6.	Jehi L. The epileptogenic zone: concept and definition. Epilepsy Curr, 2018, 18(1): 12-16.
7.	蒋忱希, 李晓楠. 脑电活动高频振荡现象定位致痫区在癫痫外科中应用的研究进展. 癫痫与神经电生理学杂志, 2017, 26(6): 376-378.
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9.	Xu C, Zhou W. Stereoelectroencephalography-guided radiofrequency thermocoagulation (SEEG-guided RF-TC) in patients with drug-resistant focal epilepsy. Transl Neurosci, 2017, 3(1): 40-47.
10.	马东华, 郑旭媛, 王真. 基于形态成分分析的癫痫脑电棘波检测. 生物医学工程学杂志, 2013, 30(4): 710-713, 723.
11.	Rajendra Acharya U, Shu L O, Yuki H, et al. Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Comput Biol Med, 2018, 100(1): 270-278.
12.	李晓楠, 杨小枫. 高频振荡自动检测方法的研究与进展. 临床神经外科杂志, 2018, 15(4): 314-317.
13.	崔刚强, 夏良斌, 梁建峰, 等. 基于小波多尺度分析和极限学习机的癫痫脑电分类算法. 生物医学工程学杂志, 2016, 33(6): 1025-1030, 1038.
14.	陈爽爽, 周卫东, 袁琦, 等. 基于多特征的颅内脑电癫痫检测方法. 中国生物医学工程学报, 2013, 32(3): 279-283.
15.	张健钊, 姜威, 贲睨烨. 基于样本熵与小波包能量特征提取和Real AdaBoost算法的正常期、癫痫间歇与发作期的脑电自动检测. 生物医学工程学杂志, 2016, 33(6): 1031-1038.
16.	刘伟楠, 刘燕, 佟宝同, 等. 基于功率谱的睡眠中癫痫发作预测. 生物医学工程学杂志, 2018, 35(3): 329-336.
17.	Wendling F, Bartolomei F, Bellanger J, et al. Epileptic fast intracerebral EEG activity: evidence for spatial decorrelation at seizure onset. Brain, 2003, 126(Pt 6): 1449-1459.
18.	Jacobs J, LeVan P, Chtillon C, et al. High frequency oscillations in intracranial EEGs mark epileptogenicity rather than lesion type. Brain, 2009, 132(Pt 4): 1022-1037.
19.	Jrad N, Kachenoura A, Merlet I, et al. Automatic detection and classification of high-frequency oscillations in depth-EEG signals. IEEE T Bio Eng, 2016, 64(9): 2230-2240.
20.	Lai D, Zhang X, Ma K, et al. Automated detection of high frequency oscillations in intracranial EEG using the combination of short-time energy and convolutional neural networks. IEEE Access, 2019, 7: 82501-82511.
21.	练欢, 但炜, 谢延风, 等. 高频振荡对致痫灶定位的研究进展. 临床神经外科杂志, 2022, 19(2): 224-227.
22.	Li A, Inati S, Zaghloul K, et al. Fragility in epileptic networks: the epileptogenic zone// 2017 American Control Conference(ACC). Seattle: IEEE, 2017: 2817-2822.
23.	Li A, Huynh C, Fitzgerald Z, et al. Neural fragility as an EEG marker of the seizure onset zone. Nat Neurosci, 2021, 24(10): 1465-1474.
24.	Jacobs J, Zijlmans M, Zelmann R, et al. High-frequency electroencephalographic oscillations correlate with outcome of epilepsy surgery. Ann Neurol, 2010, 67(2): 209-220.
25.	Tang X, Xia L Liu W, et al. Analysis of frequency domain of EEG signals in clinical location of epileptic focus. Clin EEG Neursci, 2013, 44(1): 25-30.
26.	闫秀鹏, 王海祥, 周文静, 等. 基于高频颅内脑电的致痫区定位和网络分析. 北京生物医学工程, 2017, 36(3): 229-236.
27.	姚晨, 王圆庆, 黎思娴, 等. 论癫痫症状学. 癫痫杂志, 2022, 8(2): 174-183.
28.	李尊钰, 袁冠前, 黄平, 等. 基于立体定向脑电图的颞叶致痫网络独立有效相干分析. 生物医学工程学杂志, 2019, 36(4): 541-547.
29.	Grinenko O, Li J, Mosher J C, et al. A fingerprint of the epileptogenic zone in human epilepsies. Brain, 2018, 141(1): 117-131.
30.	Yaffe R B, Borger P, Megevand P, et al. Physiology of functional and effective networks in epilepsy. Clin Neurophysiol, 2015, 126(2): 227-236.

1. Rajendra Acharya U, Fujita H, Sudarshan V K, et al. Application of entropies for automated diagnosis of epilepsy using EEG signals: A review. Know-Based Syst, 2015, 88: 85-96.
2. Beghi M, Cornaggia C M, Frigeni B, et al. Learning disorders in epilepsy. Epilepsia, 2010, 47(2): 14-18.
3. 熊敏, 苏化庆, 向明钧. 癫痫发病机制的研究进展. 中国当代医药, 2019, 26(30): 24-27.
4. Worrell G A, Litt B, Lee K H, et al. Unsupervised classification of high-frequency oscillations in human neocortical epilepsy and control patients. J Neurophysiol, 2010, 104(5): 2900-2912.
5. Piao Y S, Lu D H, Chen L, et al. Neuropathological findings in intractable epilepsy: 435 Chinese cases. Brain Pathol, 2010, 20(5): 902-908.
6. Jehi L. The epileptogenic zone: concept and definition. Epilepsy Curr, 2018, 18(1): 12-16.
7. 蒋忱希, 李晓楠. 脑电活动高频振荡现象定位致痫区在癫痫外科中应用的研究进展. 癫痫与神经电生理学杂志, 2017, 26(6): 376-378.
8. Massimo C, Francesco C, Laura C, et al. Stereoelectroencephalography in the presurgical evaluation of focal epilepsy: a retrospective analysis of 215 procedures. Neurosurgery, 2005, 57(4): 706-718.
9. Xu C, Zhou W. Stereoelectroencephalography-guided radiofrequency thermocoagulation (SEEG-guided RF-TC) in patients with drug-resistant focal epilepsy. Transl Neurosci, 2017, 3(1): 40-47.
10. 马东华, 郑旭媛, 王真. 基于形态成分分析的癫痫脑电棘波检测. 生物医学工程学杂志, 2013, 30(4): 710-713, 723.
11. Rajendra Acharya U, Shu L O, Yuki H, et al. Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Comput Biol Med, 2018, 100(1): 270-278.
12. 李晓楠, 杨小枫. 高频振荡自动检测方法的研究与进展. 临床神经外科杂志, 2018, 15(4): 314-317.
13. 崔刚强, 夏良斌, 梁建峰, 等. 基于小波多尺度分析和极限学习机的癫痫脑电分类算法. 生物医学工程学杂志, 2016, 33(6): 1025-1030, 1038.
14. 陈爽爽, 周卫东, 袁琦, 等. 基于多特征的颅内脑电癫痫检测方法. 中国生物医学工程学报, 2013, 32(3): 279-283.
15. 张健钊, 姜威, 贲睨烨. 基于样本熵与小波包能量特征提取和Real AdaBoost算法的正常期、癫痫间歇与发作期的脑电自动检测. 生物医学工程学杂志, 2016, 33(6): 1031-1038.
16. 刘伟楠, 刘燕, 佟宝同, 等. 基于功率谱的睡眠中癫痫发作预测. 生物医学工程学杂志, 2018, 35(3): 329-336.
17. Wendling F, Bartolomei F, Bellanger J, et al. Epileptic fast intracerebral EEG activity: evidence for spatial decorrelation at seizure onset. Brain, 2003, 126(Pt 6): 1449-1459.
18. Jacobs J, LeVan P, Chtillon C, et al. High frequency oscillations in intracranial EEGs mark epileptogenicity rather than lesion type. Brain, 2009, 132(Pt 4): 1022-1037.
19. Jrad N, Kachenoura A, Merlet I, et al. Automatic detection and classification of high-frequency oscillations in depth-EEG signals. IEEE T Bio Eng, 2016, 64(9): 2230-2240.
20. Lai D, Zhang X, Ma K, et al. Automated detection of high frequency oscillations in intracranial EEG using the combination of short-time energy and convolutional neural networks. IEEE Access, 2019, 7: 82501-82511.
21. 练欢, 但炜, 谢延风, 等. 高频振荡对致痫灶定位的研究进展. 临床神经外科杂志, 2022, 19(2): 224-227.
22. Li A, Inati S, Zaghloul K, et al. Fragility in epileptic networks: the epileptogenic zone// 2017 American Control Conference(ACC). Seattle: IEEE, 2017: 2817-2822.
23. Li A, Huynh C, Fitzgerald Z, et al. Neural fragility as an EEG marker of the seizure onset zone. Nat Neurosci, 2021, 24(10): 1465-1474.
24. Jacobs J, Zijlmans M, Zelmann R, et al. High-frequency electroencephalographic oscillations correlate with outcome of epilepsy surgery. Ann Neurol, 2010, 67(2): 209-220.
25. Tang X, Xia L Liu W, et al. Analysis of frequency domain of EEG signals in clinical location of epileptic focus. Clin EEG Neursci, 2013, 44(1): 25-30.
26. 闫秀鹏, 王海祥, 周文静, 等. 基于高频颅内脑电的致痫区定位和网络分析. 北京生物医学工程, 2017, 36(3): 229-236.
27. 姚晨, 王圆庆, 黎思娴, 等. 论癫痫症状学. 癫痫杂志, 2022, 8(2): 174-183.
28. 李尊钰, 袁冠前, 黄平, 等. 基于立体定向脑电图的颞叶致痫网络独立有效相干分析. 生物医学工程学杂志, 2019, 36(4): 541-547.
29. Grinenko O, Li J, Mosher J C, et al. A fingerprint of the epileptogenic zone in human epilepsies. Brain, 2018, 141(1): 117-131.
30. Yaffe R B, Borger P, Megevand P, et al. Physiology of functional and effective networks in epilepsy. Clin Neurophysiol, 2015, 126(2): 227-236.

Journal of Biomedical Engineering

Analysis of neural fragility in epileptic zone based on stereoelectroencephalography

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