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：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

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

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, 蘇化慶, 向明鈞. 癲癇發病機制的研究進展. 中國當代醫藥, 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.	蔣忱希, 李曉楠. 腦電活動高頻振蕩現象定位致癇區在癲癇外科中應用的研究進展. 癲癇與神經電生理學雜志, 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.	馬東華, 鄭旭媛, 王真. 基于形態成分分析的癲癇腦電棘波檢測. 生物醫學工程學雜志, 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.	李曉楠, 楊小楓. 高頻振蕩自動檢測方法的研究與進展. 臨床神經外科雜志, 2018, 15(4): 314-317.
13.	崔剛強, 夏良斌, 梁建峰, 等. 基于小波多尺度分析和極限學習機的癲癇腦電分類算法. 生物醫學工程學雜志, 2016, 33(6): 1025-1030, 1038.
14.	陳爽爽, 周衛東, 袁琦, 等. 基于多特征的顱內腦電癲癇檢測方法. 中國生物醫學工程學報, 2013, 32(3): 279-283.
15.	張健釗, 姜威, 賁睨燁. 基于樣本熵與小波包能量特征提取和Real AdaBoost算法的正常期、癲癇間歇與發作期的腦電自動檢測. 生物醫學工程學雜志, 2016, 33(6): 1031-1038.
16.	劉偉楠, 劉燕, 佟寶同, 等. 基于功率譜的睡眠中癲癇發作預測. 生物醫學工程學雜志, 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.	練歡, 但煒, 謝延風, 等. 高頻振蕩對致癇灶定位的研究進展. 臨床神經外科雜志, 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.	閆秀鵬, 王海祥, 周文靜, 等. 基于高頻顱內腦電的致癇區定位和網絡分析. 北京生物醫學工程, 2017, 36(3): 229-236.
27.	姚晨, 王圓慶, 黎思嫻, 等. 論癲癇癥狀學. 癲癇雜志, 2022, 8(2): 174-183.
28.	李尊鈺, 袁冠前, 黃平, 等. 基于立體定向腦電圖的顳葉致癇網絡獨立有效相干分析. 生物醫學工程學雜志, 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, 蘇化慶, 向明鈞. 癲癇發病機制的研究進展. 中國當代醫藥, 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. 蔣忱希, 李曉楠. 腦電活動高頻振蕩現象定位致癇區在癲癇外科中應用的研究進展. 癲癇與神經電生理學雜志, 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. 馬東華, 鄭旭媛, 王真. 基于形態成分分析的癲癇腦電棘波檢測. 生物醫學工程學雜志, 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. 李曉楠, 楊小楓. 高頻振蕩自動檢測方法的研究與進展. 臨床神經外科雜志, 2018, 15(4): 314-317.
13. 崔剛強, 夏良斌, 梁建峰, 等. 基于小波多尺度分析和極限學習機的癲癇腦電分類算法. 生物醫學工程學雜志, 2016, 33(6): 1025-1030, 1038.
14. 陳爽爽, 周衛東, 袁琦, 等. 基于多特征的顱內腦電癲癇檢測方法. 中國生物醫學工程學報, 2013, 32(3): 279-283.
15. 張健釗, 姜威, 賁睨燁. 基于樣本熵與小波包能量特征提取和Real AdaBoost算法的正常期、癲癇間歇與發作期的腦電自動檢測. 生物醫學工程學雜志, 2016, 33(6): 1031-1038.
16. 劉偉楠, 劉燕, 佟寶同, 等. 基于功率譜的睡眠中癲癇發作預測. 生物醫學工程學雜志, 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. 練歡, 但煒, 謝延風, 等. 高頻振蕩對致癇灶定位的研究進展. 臨床神經外科雜志, 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. 閆秀鵬, 王海祥, 周文靜, 等. 基于高頻顱內腦電的致癇區定位和網絡分析. 北京生物醫學工程, 2017, 36(3): 229-236.
27. 姚晨, 王圓慶, 黎思嫻, 等. 論癲癇癥狀學. 癲癇雜志, 2022, 8(2): 174-183.
28. 李尊鈺, 袁冠前, 黃平, 等. 基于立體定向腦電圖的顳葉致癇網絡獨立有效相干分析. 生物醫學工程學雜志, 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

Abstract Full text Figures/Tables Video References Cited by

Previous Article

Next Article

Format

Content