A research on epilepsy source localization from scalp electroencephalograph based on patient-specific head model and multi-dipole model_Journal of Biomedical Engineering

Authors：

QU Ruowei ^1,2 , WANG Zhaonan ³ , WANG Shifeng ⁴ , WANG Yao ⁵ ,  WANG Le ⁶ , YIN Shaoya ⁶ , GU Junhua ¹ , XU Guizhi ¹

1. State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300401, P. R. China;
2. School of Artificial Intelligence and Data Science, Hebei University of Technology, Tianjin 300401, P. R. China;
3. School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300130, P. R. China;
4. Tianjin Universal Medical Imaging Diagnostic Center, Tianjin 300110, P. R. China;
5. School of Electrical Engineering, Hebei University of Science, Shijiazhuang 050091, P. R. China;
6. Department of Functional Neurosurgery, Huanhu Hospital, Tianjin 300350, P. R. China;

Corresponding?author：

WANG Le, Email: wangledr@126.com

Keywords：

Epileptogenic zone localization; Biological electromagnetic inverse problem; Multi-dipole; Realistic head model; Epilepsy

DOI：

10.7507/1001-5515.202209045

Video：

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Abstract Full text Figures/Tables Video References Cited by

Accurate source localization of the epileptogenic zone (EZ) is the primary condition of surgical removal of EZ. The traditional localization results based on three-dimensional ball model or standard head model may cause errors. This study intended to localize the EZ by using the patient-specific head model and multi-dipole algorithms using spikes during sleep. Then the current density distribution on the cortex was computed and used to construct the phase transfer entropy functional connectivity network between different brain areas to obtain the localization of EZ. The experiment result showed that our improved methods could reach the accuracy of 89.27% and the number of implanted electrodes could be reduced by (19.34 ± 7.15)%. This work can not only improve the accuracy of EZ localization, but also reduce the additional injury and potential risk caused by preoperative examination and surgical operation, and provide a more intuitive and effective reference for neurosurgeons to make surgical plans.

Citation： QU Ruowei, WANG Zhaonan, WANG Shifeng, WANG Yao, WANG Le, YIN Shaoya, GU Junhua, XU Guizhi. A research on epilepsy source localization from scalp electroencephalograph based on patient-specific head model and multi-dipole model. Journal of Biomedical Engineering, 2023, 40(2): 272-279. doi: 10.7507/1001-5515.202209045 Copy

1.	Wang G, Wang D, Du C, et al. Seizure prediction using directed transfer function and convolution neural network on intracranial EEG. IEEE T Neur Sys Reh, 2020, 28(12): 2711-2720.
2.	Sumsky S, Santaniello S. Decision support system for seizure onset zone localization based on channel ranking and high-frequency EEG activity. IEEE J Biomed Health, 2019, 23(4): 1535-1545.
3.	Lin Y, Du P, Sun H, et al. Identifying refractory epilepsy without structural abnormalities by fusing the common spatial patterns of functional and effective EEG networks. IEEE T Neur Sys Reh, 2021, 29: 708-717.
4.	Li C, Sohrabpour A, Jiang H, et al. High-frequency hubs of the ictal cross-frequency coupling network predict surgical outcome in epilepsy patients. IEEE T Neur Sys Reh, 2021, 29: 1290-1299.
5.	Liu A, Thesen T, Barr W, et al. Parahippocampal and entorhinal resection extent predicts verbal memory decline in an epilepsy surgery cohort. JCN, 2017, 29(5): 869-880.
6.	保學輝, 董浩然, 劉迢迢, 等. 焦慮狀態前額葉皮層動作電位因果網絡連接模式的研究. 生物醫學工程學雜志, 2020, 37(3): 389-398.
7.	Celik H, Acikel B, Ozdemir M, et al. Evaluation of the clinical characteristics of children with autism spectrum disorder and epilepsy and the perception of their parents on quality of life. Epilepsy Res, 2021, 172(1): 106-119.
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12.	Kaiboriboon K, HO L, Hamaneh M, et al. EEG source imaging in epilepsy-practicalities and pitfalls. Nat Rev Neurol, 2012, 8(9): 498-507.
13.	Menana H. 3-D FEM–BEM coupling for the magnetic field computation in thin shells: application to the evaluation of ship magnetic signature. IEEE T Magn, 2021, 57(6): 1-6.
14.	Haghighatpanah N, Gohary R. Novel recovery algorithms for block sparse signals with known and unknown borders. IEEE T Signal Process, 2022, 70: 3726-3742.
15.	謝沖, 葛曼玲, 付曉璇, 等. 靜息態功能磁共振成像多尺度熵優化及在顳葉癲癇致癇側定位中的應用. 生物醫學工程學雜志, 2021, 38(6): 1163-1172.
16.	尹寧, 張家皓, 王海力, 等. 磁刺激穴位調節負性情緒的腦電溯源和腦網絡研究. 電工技術學報, 2021, 36(4): 756-764.
17.	Ingrid S, Samuel B, Giuseppe C, et al. ILAE classification of the epilepsies: position paper of the ILAE commission for classification and terminology. Epilepsia, 2017, 58(4): 512-521.
18.	劉蒙蒙, 徐桂芝, 于洪麗, 等. 經顱直流電刺激下腦卒中患者腦電功率譜密度研究. 生物醫學工程學雜志, 2022, 39(3): 498-506.
19.	Gao Tong, Chen Hao, Chen Wen. HTGCE: An graph-based and classifier-oriented dimension reduction method for multisource heterogeneous feature tensors. IEEE Trans Geosci Remote Sens, 2022, 60: 1-18.
20.	Sabel B, Kresinsky A, Morals L, et al. Evaluating current density modeling of non-invasive eye and brain electrical stimulation using phosphene thresholds. IEEE T Neur Sys Reh, 2021, 29: 2133-2141.
21.	Dolabdjian C, Cordier C. Analysis by systemic approach of magnetic dipole source location performances by using an IoT software gradiometer head. IEEE Sensors J, 2022, 22(8): 7709-7716.
22.	Cao S, Zhang X, Xiang W, et al. A power flow transfer entropy based AC fault detection method for the MTDC wind power integration system. IEEE Trans Ind Electron, 2021, 68(11): 11614-11620.
23.	王振宇, 薛青, 熊秀春, 等. 基于靜息態腦電的心因性非癲癇性發作患者腦功能網絡分析及分類識別研究. 生物醫學工程學雜志, 2015, 32(1): 8-12.
24.	袁勤, 蔣濤. 基于格蘭杰因果方法的注意腦電網絡分析. 生物醫學工程學雜志, 2016, 33(1): 56-60.
25.	Giri A, Kumar L, Gandhi T. Brain source localization in head harmonics domain. IEEE T Instrum Meas, 2021, 70: 1-10.

1. Wang G, Wang D, Du C, et al. Seizure prediction using directed transfer function and convolution neural network on intracranial EEG. IEEE T Neur Sys Reh, 2020, 28(12): 2711-2720.
2. Sumsky S, Santaniello S. Decision support system for seizure onset zone localization based on channel ranking and high-frequency EEG activity. IEEE J Biomed Health, 2019, 23(4): 1535-1545.
3. Lin Y, Du P, Sun H, et al. Identifying refractory epilepsy without structural abnormalities by fusing the common spatial patterns of functional and effective EEG networks. IEEE T Neur Sys Reh, 2021, 29: 708-717.
4. Li C, Sohrabpour A, Jiang H, et al. High-frequency hubs of the ictal cross-frequency coupling network predict surgical outcome in epilepsy patients. IEEE T Neur Sys Reh, 2021, 29: 1290-1299.
5. Liu A, Thesen T, Barr W, et al. Parahippocampal and entorhinal resection extent predicts verbal memory decline in an epilepsy surgery cohort. JCN, 2017, 29(5): 869-880.
6. 保學輝, 董浩然, 劉迢迢, 等. 焦慮狀態前額葉皮層動作電位因果網絡連接模式的研究. 生物醫學工程學雜志, 2020, 37(3): 389-398.
7. Celik H, Acikel B, Ozdemir M, et al. Evaluation of the clinical characteristics of children with autism spectrum disorder and epilepsy and the perception of their parents on quality of life. Epilepsy Res, 2021, 172(1): 106-119.
8. Boux F, Forbes F, Arbel J, et al. Bayesian inverse regression for vascular magnetic resonance fingerprinting. IEEE T Med, 2021, 40(7): 1827-1837.
9. Bayram M, Arserim M A. Analysis of epileptic iEEG data by applying convolutional neural networks to low-frequency scalograms. IEEE Access, 2021, 9: 162520-162529.
10. Xu H, Dong M, Lee M H, et al. Objective detection of eloquent axonal pathways to minimize postoperative deficits in pediatric epilepsy surgery using diffusion tractography and convolutional neural networks. IEEE T Med, 2019, 38(8): 1910-1922.
11. Duan L, Wang Z, Qiao Y, et al. An automatic method for epileptic seizure detection based on deep metric learning. IEEE J Biomed Health, 2022, 26(5): 2147-2157.
12. Kaiboriboon K, HO L, Hamaneh M, et al. EEG source imaging in epilepsy-practicalities and pitfalls. Nat Rev Neurol, 2012, 8(9): 498-507.
13. Menana H. 3-D FEM–BEM coupling for the magnetic field computation in thin shells: application to the evaluation of ship magnetic signature. IEEE T Magn, 2021, 57(6): 1-6.
14. Haghighatpanah N, Gohary R. Novel recovery algorithms for block sparse signals with known and unknown borders. IEEE T Signal Process, 2022, 70: 3726-3742.
15. 謝沖, 葛曼玲, 付曉璇, 等. 靜息態功能磁共振成像多尺度熵優化及在顳葉癲癇致癇側定位中的應用. 生物醫學工程學雜志, 2021, 38(6): 1163-1172.
16. 尹寧, 張家皓, 王海力, 等. 磁刺激穴位調節負性情緒的腦電溯源和腦網絡研究. 電工技術學報, 2021, 36(4): 756-764.
17. Ingrid S, Samuel B, Giuseppe C, et al. ILAE classification of the epilepsies: position paper of the ILAE commission for classification and terminology. Epilepsia, 2017, 58(4): 512-521.
18. 劉蒙蒙, 徐桂芝, 于洪麗, 等. 經顱直流電刺激下腦卒中患者腦電功率譜密度研究. 生物醫學工程學雜志, 2022, 39(3): 498-506.
19. Gao Tong, Chen Hao, Chen Wen. HTGCE: An graph-based and classifier-oriented dimension reduction method for multisource heterogeneous feature tensors. IEEE Trans Geosci Remote Sens, 2022, 60: 1-18.
20. Sabel B, Kresinsky A, Morals L, et al. Evaluating current density modeling of non-invasive eye and brain electrical stimulation using phosphene thresholds. IEEE T Neur Sys Reh, 2021, 29: 2133-2141.
21. Dolabdjian C, Cordier C. Analysis by systemic approach of magnetic dipole source location performances by using an IoT software gradiometer head. IEEE Sensors J, 2022, 22(8): 7709-7716.
22. Cao S, Zhang X, Xiang W, et al. A power flow transfer entropy based AC fault detection method for the MTDC wind power integration system. IEEE Trans Ind Electron, 2021, 68(11): 11614-11620.
23. 王振宇, 薛青, 熊秀春, 等. 基于靜息態腦電的心因性非癲癇性發作患者腦功能網絡分析及分類識別研究. 生物醫學工程學雜志, 2015, 32(1): 8-12.
24. 袁勤, 蔣濤. 基于格蘭杰因果方法的注意腦電網絡分析. 生物醫學工程學雜志, 2016, 33(1): 56-60.
25. Giri A, Kumar L, Gandhi T. Brain source localization in head harmonics domain. IEEE T Instrum Meas, 2021, 70: 1-10.

Journal of Biomedical Engineering

A research on epilepsy source localization from scalp electroencephalograph based on patient-specific head model and multi-dipole model

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