Developments of ex vivo cardiac electrical mapping and intelligent labeling of atrial fibrillation substrates_Journal of Biomedical Engineering

Authors：

CHANG Yi ¹ ,  DONG Ming ¹ , WANG Bin ¹ , FAN Lihong ²

1. State Key Library of Electrical Insulation and Power Equipment, Xi’an Jiaotong University, Xi’an 710049, P. R. China;
2. The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710061, P. R. China;

Corresponding author：

DONG Ming, Email: dongming@xjtu.edu.cn

Keywords：

Atrial fibrillation; Electrophysiological labeling; Electrocardiogram forward problem; Electrocardiogram inverse problem; Deep learning

DOI：

10.7507/1001-5515.202211046

Video：

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Cardiac three-dimensional electrophysiological labeling technology is the prerequisite and foundation of atrial fibrillation (AF) ablation surgery, and invasive labeling is the current clinical method, but there are many shortcomings such as large trauma, long procedure duration, and low success rate. In recent years, because of its non-invasive and convenient characteristics, ex vivo labeling has become a new direction for the development of electrophysiological labeling technology. With the rapid development of computer hardware and software as well as the accumulation of clinical database, the application of deep learning technology in electrocardiogram (ECG) data is becoming more extensive and has made great progress, which provides new ideas for the research of ex vivo cardiac mapping and intelligent labeling of AF substrates. This paper reviewed the research progress in the fields of ECG forward problem, ECG inverse problem, and the application of deep learning in AF labeling, discussed the problems of ex vivo intelligent labeling of AF substrates and the possible approaches to solve them, prospected the challenges and future directions for ex vivo cardiac electrophysiology labeling.

Citation： CHANG Yi, DONG Ming, WANG Bin, FAN Lihong. Developments of ex vivo cardiac electrical mapping and intelligent labeling of atrial fibrillation substrates. Journal of Biomedical Engineering, 2024, 41(1): 184-190. doi: 10.7507/1001-5515.202211046 Copy

1.	Kornej J, Huang Q, Preis S R, et al. Temporal trends in cause-specific mortality among individuals with newly diagnosed atrial fibrillation in the Framingham Heart Study. BMC Med, 2021, 19(1): 170.
2.	中华医学会心电生理和起搏分会, 中国医师协会心律学专业委员会, 中国房颤中心联盟心房颤动防治专家工作委员会. 心房颤动: 目前的认识和治疗建议(2021). 中华心律失常学杂志, 2022, 26(1): 15-88.
3.	马凌, 马明仁, 张鹏, 等. 房颤触发机制研究新进展. 实用心电学杂志, 2021, 30(6): 392-397.
4.	Bai J, Zhao J, Ni H, et al. Editorial: Diagnosis, monitoring, and treatment of heart rhythm: new insights and novel computational methods. Front Physiol, 2023, 14: 1272377.
5.	Mikhailov A V, Kalyanasundaram A, Li N, et al. Comprehensive evaluation of electrophysiological and 3D structural features of human atrial myocardium with insights on atrial fibrillation maintenance mechanisms. J Mol Cell Cardiol, 2021, 151: 56-71.
6.	娄洋, 周鑫斌, 毛威. 心电成像技术临床应用展望. 心电与循环, 2021, 40(6): 559-564.
7.	Ehrlich M P, Laufer G, Coti I, et al. Noninvasive mapping before surgical ablation for persistent, long-standing atrial fibrillation. J Thorac Cardiovasc Surg, 2019, 157(1): 248-256.
8.	Guillem M S, Climent A M, Millet J, et al. Noninvasive localization of maximal frequency sites of atrial fibrillation by body surface potential mapping. Circ Arrhythm Electrophysiol, 2013, 6(2): 294-301.
9.	Ramanathan C, Ghanem R N, Jia P, et al. Noninvasive electrocardiographic imaging for cardiac electrophysiology and arrhythmia. Nat Med, 2004, 10(4): 422-428.
10.	Hong S, Zhou Y, Shang J, et al. Opportunities and challenges of deep learning methods for electrocardiogram data: A systematic review. Comput Biol Med, 2020, 122: 103801.
11.	Qu R, Wang Z, Wang S, et al. Interictal electrophysiological source imaging based on realistic epilepsy head model in presurgical evaluation: A prospective study. Chin J Electr, 2023, 9(1): 61-70.
12.	Clayton R H, Aboelkassem Y, Cantwell C D, et al. An audit of uncertainty in multi-scale cardiac electrophysiology models. Philos Trans Royal Soc, 2020, 378(2173): 20190335.
13.	Nagel C, Espinosa C B, Gillette K, et al. Comparison of propagation models and forward calculation methods on cellular, tissue and organ scale atrial electrophysiology. IEEE Trans Biomed Eng, 2023, 70(2): 511-522.
14.	Morotti S, Liu C, Hegyi B, et al. Quantitative cross-species translators of cardiac myocyte electrophysiology: Model training, experimental validation, and applications. Sci Adv, 2021, 7(47): eabg0927.
15.	Lei C L, Clerx M, Beattie K A, et al. Rapid characterization of hERG channel kinetics II: Temperature dependence. Biophys J, 2019, 117(12): 2455-2470.
16.	Fastl T E, Tobon-Gomez C, Crozier A, et al. Personalized computational modeling of left atrial geometry and transmural myofiber architecture. Med Image Anal, 2018, 47: 180-190.
17.	Corrado C, Williams S, Karim R, et al. A work flow to build and validate patient specific left atrium electrophysiology models from catheter measurements. Med Image Anal, 2018, 47: 153-163.
18.	Biasi N, Seghetti P, Mercati M, et al. A smoothed boundary bidomain model for cardiac simulations in anatomically detailed geometries. PLoS One, 2023, 18(6): e0286577.
19.	Sovilj S, Čeperič V, Magjarevič R. 3D cardiac electrical activity model. Automatika, 2016, 57(2): 540-548.
20.	Franzone P C, Guerri L. Spreading of excitation in 3-D models of the anisotropic cardiac tissue. I. Validation of the Eikonal model. Math Biosci, 1993, 113(2): 145-209.
21.	Neic A, Campos F O, Prassl A J, et al. Efficient computation of electrograms and ECGs in human whole heart simulations using a Reaction-Eikonal model. J Comput Phys, 2017, 346: 191-211.
22.	Gonzalez Herrero M E, Kuehn C, Tsaneva-Atanasova K. Reduced models of cardiomyocytes excitability: Comparing Karma and FitzHugh–Nagumo. Bull Math Biol, 2021, 83(8): 88.
23.	Khan R, Ng K T. Numerical study of POD-Galerkin-DEIM reduced order modeling of cardiac monodomain formulation. Biomed Phys Eng Express, 2021, 8(1): 015012.
24.	Han B, Trew M L, Zgierski-Johnston C M. Cardiac conduction Velocity, remodeling and arrhythmogenesis. Cells, 2021, 10(11): 2923.
25.	Pagani S, Dede’ L, Frontera A, et al. A computational study of the electrophysiological substrate in patients suffering from atrial fibrillation. Front Physiol, 2021, 12: 673612.
26.	Salinet J, Molero R, Schlindwein F S, et al. Electrocardiographic imaging for atrial fibrillation: A perspective from computer models and animal experiments to clinical value. Front Physiol, 2021, 12: 653013.
27.	Wang T, Karel J, Bonizzi P, et al. Influence of the Tikhonov regularization parameter on the accuracy of the inverse problem in electrocardiography. Sensors, 2023, 23(4): 1841.
28.	Karoui A, Bear L, Migerditichan P, et al. Evaluation of fifteen algorithms for the resolution of the electrocardiography imaging inverse problem using ex-vivo and in-silico data. Front Physiol, 2018, 9: 1708.
29.	Chen R, Li J, Wu J. A robust algorithm for selecting optimal regularization parameter based on bilateral accumulative area// 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Berlin: IEEE, 2019: 4893-4896.
30.	Yao B, Yang H. Spatiotemporal regularization for inverse ECG modeling. IISE Trans Healthc Syst Eng, 2021, 11(1): 11-23.
31.	Lan S, Li S, Pasha M. Bayesian spatiotemporal modeling for inverse problems. Stat Comput, 2023, 33: 89.
32.	贺高, 蒋明峰, 郑俊褒, 等. 卷积神经网络在心电逆问题中的应用. 计算机工程与应用, 2019, 55(1): 123-127,265.
33.	Bacoyannis T, Ly B, Cedilnik N, et al. Deep learning formulation of electrocardiographic imaging integrating image and signal information with data-driven regularization. Europace, 2021, 23(Supplement_1): i55-i62.
34.	Lee K-S, Jung S, Gil Y, et al. Atrial fibrillation classification based on convolutional neural networks. BMC Medical Inform Decis Mak, 2019, 19(1): 206.
35.	Xia Q, Yao Y, Hu Z, et al. Automatic 3D atrial segmentation from GE-MRIs using volumetric fully convolutional networks// Pop M, Sermesant M, Zhao J, et al. Statistical atlases and computational models of the heart. Atrial segmentation and LV quantification challenges. Cham: Springer International Publishing, 2019, 11395: 211-220.
36.	Puybareau É, Zhao Z, Khoudli Y, et al. Left atrial segmentation in a few seconds using fully convolutional network and transfer learning// Pop M, Sermesant M, Zhao J, et al. Statistical atlases and computational models of the heart. Atrial segmentation and LV quantification challenges. Cham: Springer International Publishing, 2019, 11395: 339-347.
37.	Liu Y, Dai Y, Yan C, et al. Deep learning based method for left atrial segmentation in GE-MRI// Pop M, Sermesant M, Zhao J, et al. Statistical atlases and computational models of the heart. Atrial segmentation and LV quantification challenges. Cham: Springer International Publishing, 2019, 11395: 311-318.
38.	McGillivray M F, Cheng W, Peters N S, et al. Machine learning methods for locating re-entrant drivers from electrograms in a model of atrial fibrillation. Royal Soc Open Sci, 2018, 5(4): 172434.
39.	Zolotarev A M, Hansen B J, Ivanova E A, et al. Optical mapping-validated machine learning improves atrial fibrillation driver detection by multi-electrode mapping. Circ Arrhythm Electrophysiol, 2020, 13(10): e008249.
40.	Alhusseini M I, Abuzaid F, Rogers A J, et al. Machine learning to classify intracardiac electrical patterns during atrial fibrillation: Machine learning of atrial fibrillation. Circ Arrhythm Electrophysiol, 2020, 13(8): e008160.
41.	Liao S, Ragot D, Nayyar S, et al. Deep learning classification of unipolar electrograms in human atrial fibrillation: Application in focal source mapping. Front Physiol, 2021, 12: 704122.
42.	Ríos-Muñoz G R, Fernández-Avilés F, Arenal Á. Convolutional neural networks for mechanistic driver detection in atrial fibrillation. Int J Mol Sci, 2022, 23(8): 4216.
43.	Weder M, Hegemann D, Amberg M, et al. Embroidered electrode with silver/titanium coating for long-term ECG monitoring. Sensors, 2015, 15(1): 1750-1759.
44.	Yang G, Hu Y, Guo W, et al. Tunable hydrogel electronics for diagnosis of peripheral neuropathy. Adv Mater, 2023: e2308831.
45.	Gander L, Krause R, Multerer M, et al. Space–time shape uncertainties in the forward and inverse problem of electrocardiography. Int J Numer Method Biomed Eng, 2021, 37(10): e3522.
46.	Bergquist J, Rupp L, Zenger B, et al. Body surface potential mapping: Contemporary applications and future perspectives. Hearts, 2021, 2(4): 514-542.
47.	Ghazarian A, Zheng J, Struppa D, et al. Assessing the reidentification risks posed by deep learning algorithms applied to ECG data. IEEE Access, 2022, 10: 68711-68723.
48.	Huang P, Guo L, Li M, et al. Practical privacy preserving ECG-based authentication for IoT-based healthcare. IEEE Internet Things J, 2019, 6(5): 9200-9210.

1. Kornej J, Huang Q, Preis S R, et al. Temporal trends in cause-specific mortality among individuals with newly diagnosed atrial fibrillation in the Framingham Heart Study. BMC Med, 2021, 19(1): 170.
2. 中华医学会心电生理和起搏分会, 中国医师协会心律学专业委员会, 中国房颤中心联盟心房颤动防治专家工作委员会. 心房颤动: 目前的认识和治疗建议(2021). 中华心律失常学杂志, 2022, 26(1): 15-88.
3. 马凌, 马明仁, 张鹏, 等. 房颤触发机制研究新进展. 实用心电学杂志, 2021, 30(6): 392-397.
4. Bai J, Zhao J, Ni H, et al. Editorial: Diagnosis, monitoring, and treatment of heart rhythm: new insights and novel computational methods. Front Physiol, 2023, 14: 1272377.
5. Mikhailov A V, Kalyanasundaram A, Li N, et al. Comprehensive evaluation of electrophysiological and 3D structural features of human atrial myocardium with insights on atrial fibrillation maintenance mechanisms. J Mol Cell Cardiol, 2021, 151: 56-71.
6. 娄洋, 周鑫斌, 毛威. 心电成像技术临床应用展望. 心电与循环, 2021, 40(6): 559-564.
7. Ehrlich M P, Laufer G, Coti I, et al. Noninvasive mapping before surgical ablation for persistent, long-standing atrial fibrillation. J Thorac Cardiovasc Surg, 2019, 157(1): 248-256.
8. Guillem M S, Climent A M, Millet J, et al. Noninvasive localization of maximal frequency sites of atrial fibrillation by body surface potential mapping. Circ Arrhythm Electrophysiol, 2013, 6(2): 294-301.
9. Ramanathan C, Ghanem R N, Jia P, et al. Noninvasive electrocardiographic imaging for cardiac electrophysiology and arrhythmia. Nat Med, 2004, 10(4): 422-428.
10. Hong S, Zhou Y, Shang J, et al. Opportunities and challenges of deep learning methods for electrocardiogram data: A systematic review. Comput Biol Med, 2020, 122: 103801.
11. Qu R, Wang Z, Wang S, et al. Interictal electrophysiological source imaging based on realistic epilepsy head model in presurgical evaluation: A prospective study. Chin J Electr, 2023, 9(1): 61-70.
12. Clayton R H, Aboelkassem Y, Cantwell C D, et al. An audit of uncertainty in multi-scale cardiac electrophysiology models. Philos Trans Royal Soc, 2020, 378(2173): 20190335.
13. Nagel C, Espinosa C B, Gillette K, et al. Comparison of propagation models and forward calculation methods on cellular, tissue and organ scale atrial electrophysiology. IEEE Trans Biomed Eng, 2023, 70(2): 511-522.
14. Morotti S, Liu C, Hegyi B, et al. Quantitative cross-species translators of cardiac myocyte electrophysiology: Model training, experimental validation, and applications. Sci Adv, 2021, 7(47): eabg0927.
15. Lei C L, Clerx M, Beattie K A, et al. Rapid characterization of hERG channel kinetics II: Temperature dependence. Biophys J, 2019, 117(12): 2455-2470.
16. Fastl T E, Tobon-Gomez C, Crozier A, et al. Personalized computational modeling of left atrial geometry and transmural myofiber architecture. Med Image Anal, 2018, 47: 180-190.
17. Corrado C, Williams S, Karim R, et al. A work flow to build and validate patient specific left atrium electrophysiology models from catheter measurements. Med Image Anal, 2018, 47: 153-163.
18. Biasi N, Seghetti P, Mercati M, et al. A smoothed boundary bidomain model for cardiac simulations in anatomically detailed geometries. PLoS One, 2023, 18(6): e0286577.
19. Sovilj S, Čeperič V, Magjarevič R. 3D cardiac electrical activity model. Automatika, 2016, 57(2): 540-548.
20. Franzone P C, Guerri L. Spreading of excitation in 3-D models of the anisotropic cardiac tissue. I. Validation of the Eikonal model. Math Biosci, 1993, 113(2): 145-209.
21. Neic A, Campos F O, Prassl A J, et al. Efficient computation of electrograms and ECGs in human whole heart simulations using a Reaction-Eikonal model. J Comput Phys, 2017, 346: 191-211.
22. Gonzalez Herrero M E, Kuehn C, Tsaneva-Atanasova K. Reduced models of cardiomyocytes excitability: Comparing Karma and FitzHugh–Nagumo. Bull Math Biol, 2021, 83(8): 88.
23. Khan R, Ng K T. Numerical study of POD-Galerkin-DEIM reduced order modeling of cardiac monodomain formulation. Biomed Phys Eng Express, 2021, 8(1): 015012.
24. Han B, Trew M L, Zgierski-Johnston C M. Cardiac conduction Velocity, remodeling and arrhythmogenesis. Cells, 2021, 10(11): 2923.
25. Pagani S, Dede’ L, Frontera A, et al. A computational study of the electrophysiological substrate in patients suffering from atrial fibrillation. Front Physiol, 2021, 12: 673612.
26. Salinet J, Molero R, Schlindwein F S, et al. Electrocardiographic imaging for atrial fibrillation: A perspective from computer models and animal experiments to clinical value. Front Physiol, 2021, 12: 653013.
27. Wang T, Karel J, Bonizzi P, et al. Influence of the Tikhonov regularization parameter on the accuracy of the inverse problem in electrocardiography. Sensors, 2023, 23(4): 1841.
28. Karoui A, Bear L, Migerditichan P, et al. Evaluation of fifteen algorithms for the resolution of the electrocardiography imaging inverse problem using ex-vivo and in-silico data. Front Physiol, 2018, 9: 1708.
29. Chen R, Li J, Wu J. A robust algorithm for selecting optimal regularization parameter based on bilateral accumulative area// 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Berlin: IEEE, 2019: 4893-4896.
30. Yao B, Yang H. Spatiotemporal regularization for inverse ECG modeling. IISE Trans Healthc Syst Eng, 2021, 11(1): 11-23.
31. Lan S, Li S, Pasha M. Bayesian spatiotemporal modeling for inverse problems. Stat Comput, 2023, 33: 89.
32. 贺高, 蒋明峰, 郑俊褒, 等. 卷积神经网络在心电逆问题中的应用. 计算机工程与应用, 2019, 55(1): 123-127,265.
33. Bacoyannis T, Ly B, Cedilnik N, et al. Deep learning formulation of electrocardiographic imaging integrating image and signal information with data-driven regularization. Europace, 2021, 23(Supplement_1): i55-i62.
34. Lee K-S, Jung S, Gil Y, et al. Atrial fibrillation classification based on convolutional neural networks. BMC Medical Inform Decis Mak, 2019, 19(1): 206.
35. Xia Q, Yao Y, Hu Z, et al. Automatic 3D atrial segmentation from GE-MRIs using volumetric fully convolutional networks// Pop M, Sermesant M, Zhao J, et al. Statistical atlases and computational models of the heart. Atrial segmentation and LV quantification challenges. Cham: Springer International Publishing, 2019, 11395: 211-220.
36. Puybareau É, Zhao Z, Khoudli Y, et al. Left atrial segmentation in a few seconds using fully convolutional network and transfer learning// Pop M, Sermesant M, Zhao J, et al. Statistical atlases and computational models of the heart. Atrial segmentation and LV quantification challenges. Cham: Springer International Publishing, 2019, 11395: 339-347.
37. Liu Y, Dai Y, Yan C, et al. Deep learning based method for left atrial segmentation in GE-MRI// Pop M, Sermesant M, Zhao J, et al. Statistical atlases and computational models of the heart. Atrial segmentation and LV quantification challenges. Cham: Springer International Publishing, 2019, 11395: 311-318.
38. McGillivray M F, Cheng W, Peters N S, et al. Machine learning methods for locating re-entrant drivers from electrograms in a model of atrial fibrillation. Royal Soc Open Sci, 2018, 5(4): 172434.
39. Zolotarev A M, Hansen B J, Ivanova E A, et al. Optical mapping-validated machine learning improves atrial fibrillation driver detection by multi-electrode mapping. Circ Arrhythm Electrophysiol, 2020, 13(10): e008249.
40. Alhusseini M I, Abuzaid F, Rogers A J, et al. Machine learning to classify intracardiac electrical patterns during atrial fibrillation: Machine learning of atrial fibrillation. Circ Arrhythm Electrophysiol, 2020, 13(8): e008160.
41. Liao S, Ragot D, Nayyar S, et al. Deep learning classification of unipolar electrograms in human atrial fibrillation: Application in focal source mapping. Front Physiol, 2021, 12: 704122.
42. Ríos-Muñoz G R, Fernández-Avilés F, Arenal Á. Convolutional neural networks for mechanistic driver detection in atrial fibrillation. Int J Mol Sci, 2022, 23(8): 4216.
43. Weder M, Hegemann D, Amberg M, et al. Embroidered electrode with silver/titanium coating for long-term ECG monitoring. Sensors, 2015, 15(1): 1750-1759.
44. Yang G, Hu Y, Guo W, et al. Tunable hydrogel electronics for diagnosis of peripheral neuropathy. Adv Mater, 2023: e2308831.
45. Gander L, Krause R, Multerer M, et al. Space–time shape uncertainties in the forward and inverse problem of electrocardiography. Int J Numer Method Biomed Eng, 2021, 37(10): e3522.
46. Bergquist J, Rupp L, Zenger B, et al. Body surface potential mapping: Contemporary applications and future perspectives. Hearts, 2021, 2(4): 514-542.
47. Ghazarian A, Zheng J, Struppa D, et al. Assessing the reidentification risks posed by deep learning algorithms applied to ECG data. IEEE Access, 2022, 10: 68711-68723.
48. Huang P, Guo L, Li M, et al. Practical privacy preserving ECG-based authentication for IoT-based healthcare. IEEE Internet Things J, 2019, 6(5): 9200-9210.

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