中国电气工程学报（英文） ›› 2023, Vol. 9 ›› Issue (1): 93-103.doi: 10.23919/CJEE.2023.000008
-
收稿日期:2022-09-30修回日期:2022-12-31接受日期:2023-01-30出版日期:2023-03-25发布日期:2023-04-06
Review of Ex Vivo Cardiac Electrical Mapping and Intelligent Labeling of Atrial Fibrillation Substrates*
Yi Chang1, Ming Dong1,*, Bin Wang1, Ming Ren1, Lihong Fan2
- 1. School of Electrical Engineering, Xi'an Jiaotong University, Xi'an 710049, China;
2. The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China
-
Received:2022-09-30Revised:2022-12-31Accepted:2023-01-30Online:2023-03-25Published:2023-04-06 -
Contact:*E-mail: dongming@xjtu.edu.cn -
About author:Yi Chang received his B.S. degree in Electrical Engineering from Xi'an Jiaotong University, Xi'an, China, in 2022. And he is currently working toward the M.S. degree in Electrical Engineering, Xi'an Jiaotong University, Xi'an, China. His research work is ex vivo electrophysiological monitoring.
Ming Dong (M'08) was born in Shaanxi Province, China, in 1978. He received the B.S. and Ph.D. degrees in Electrical Engineering from Xi'an Jiaotong University, Xi'an, China, in 2001 and 2006. His major is high voltage engineering and his research work are intelligent HV equipment and power condition assessment.
Bin Wang was born in Shandong Province, China, in 1995. He received the B.S. degree in Electrical Engineering from Xi'an Jiaotong University, Xi'an, China in 2017, where he is presently pursuing for the Ph.D. degree. His major is high-voltage engineering, and his present research works are HV equipment intelligent and on-site detection technology of electric power equipment.
Ming Ren was born in Shaanxi Province, China, in 1987. He received the B.S. degree in Electrical Engineering from Xi'an Jiaotong University, Xi'an, China, in 2009, where he is currently pursuing the Ph.D. degree under the guidance of Prof. Qiu. His major is high voltage engineering and his research work are fault diagnosis and state maintenance of power system.
Lihong Fan was born in Shaanxi Province, China, in 1977. She received B.S. and Ph.D. & M.D. from School of Pharmacy, Xi'an Jiaotong University, Xi'an, China, in 2001 and 2008. Her major study is cardiac electrophysiology. -
Supported by:*National Natural Science Foundation of China under Grants 62176208.
引用本文
. [J]. 中国电气工程学报（英文）, 2023, 9(1): 93-103.
Yi Chang, Ming Dong, Bin Wang, Ming Ren, Lihong Fan. Review of Ex Vivo Cardiac Electrical Mapping and Intelligent Labeling of Atrial Fibrillation Substrates*[J]. Chinese Journal of Electrical Engineering, 2023, 9(1): 93-103.
| [1] J J Rieta, F Ravelli, L Sörnmo.Advances in modeling and characterization of atrial arrhythmias.Biomedical Signal Processing and Control, 2013, 8(6): 956-957. [2] D M Lloyd-Jones, T J Wang, E P Leip, et al. Lifetime risk for development of atrial fibrillation: The framingham heart study.Circulation, 2004, 110(9): 1042-1046. [3] S S Chugh, R Havmoeller, K Narayanan, et al.Worldwide epidemiology of atrial fibrillation: A global burden of disease 2010 study.Circulation, 2014, 129(8): 837-847. [4] A V Mikhailov, A Kalyanasundaram, N Li, et al.Comprehensive evaluation of electrophysiological and 3D structural features of human atrial myocardium with insights on atrial fibrillation maintenance mechanisms.Journal of Molecular and Cellular Cardiology, 2021, 151: 56-71. [5] L Hu, D Bell, S Antani, et al.An observational study of deep learning and automated evaluation of cervical images for cancer screening.Journal of the National Cancer Institute, 2019, 111(9): 923-932. [6] S Hong, Y Zhou, J Shang, et al.Opportunities and challenges of deep learning methods for electrocardiogram data: A systematic review.Computers in Biology and Medicine, 2020, 122: 103801. [7] M R Avendi, A Kheradvar, H Jafarkhani.Automatic segmentation of the right ventricle from cardiac MRI using a learning-based approach: Automatic segmentation using a learning-based approach.Magnetic Resonance in Medicine, 2017, 78(6): 2439-2448. [8] W Bai, M Sinclair, G Tarroni, et al.Automated cardiovascular magnetic resonance image analysis with fully convolutional networks.Journal of Cardiovascular Magnetic Resonance, 2018, 20(1): 65. [9] Y H Kim, S A Chen, S Ernst, et al.2019 APHRS expert consensus statement on three-dimensional mapping systems for tachycardia developed in collaboration with HRS, EHRA, and LAHRS.Journal of Arrhythmia, 2020, 36(2): 215-270. [10] Chinese Society of Pacing and Electrophysiology, Chinese Society of Arrhythmias, Atrial Fibrillation Center Union of China. Current knowledge and management of atrial fibrillation: Consensus of Chinese experts 2021.Chinese Journal of Cardiac Arrhythmias, 2022, 26(1): 15-88. [11] R S Wijesurendra, B Casadei.Mechanisms of atrial fibrillation.Heart, 2019, 105(24): 1860-1867. [12] L Ma, M Ma, P Zhang, et al.New research progress in triggering mechanism of atrial fibrillation.Journal of Practical Electrocardiology, 2021, 30(6): 392-397. [13] M Haïssaguerre, P Jaïs, D C Shah, et al.Spontaneous initiation of atrial fibrillation by ectopic beats originating in the pulmonary veins.New England Journal of Medicine, 1998, 339(10): 659-666. [14] R Mandapati, A Skanes, J Chen, et al.Stable microreentrant sources as a mechanism of atrial fibrillation in the isolated sheep heart.Circulation, 2000, 101(2): 194-199. [15] J A Zaman, A A Grace, S M Narayan.Future directions for mapping atrial fibrillation.Arrhythmia & Electrophysiology Review, 2022, 11: e08. [16] G K Moe, J A Abildskov.Atrial fibrillation as a self-sustaining arrhythmia independent of focal discharge.American Heart Journal, 1959, 58(1): 59-70. [17] S M Narayan, D E Krummen, K Shivkumar, et al.Treatment of atrial fibrillation by the ablation of localized sources.Journal of the American College of Cardiology, 2012, 60(7): 628-636. [18] S M Narayan, D E Krummen, M W Enyeart, et al.Computational mapping identifies localized mechanisms for ablation of atrial fibrillation. PLoS One, 2012, 7(9): e46034. [19] O Berenfeld, J Jalife.Mechanisms of atrial fibrillation: Rotors, ionic determinants, and excitation frequency.Cardiology Clinics, 2014, 32(4): 495-506. [20] L Peng, W Han.Research progress on the mechanism of atrial fibrosis in atrial fibrillation.Chinese Journal of Evidence-Based Cardiovascular Medicine, 2022, 14(2): 244-246. [21] Y Zou, X Hu.The correlation between left atrial low-voltage and atrial fibrillation.Advances in Cardiovascular Diseases, 2022, 43(3): 211-213. [22] Q Zhao, B Wang, H Chu.Classification of left atrial appendage potential and influence factors in patients with persistent atrial fibrillation.Journal of Electrocardiology and Circulation, 2020, 39(4): 369-373, 378. [23] J Li, X Shi, H Guo, et al.Dynamic comparisons of epicardial electrograms between the left atrium and pulmonary veins in atrial fibrillation in goat model.Chinese Journal of Applied Physiology, 2021, 37(3): 235-239. [24] C Huang.Development of cardiac electrophysiology and future perspectives.Journal of China Medical University, 2014, 43(3): 193-195. [25] J C Balt, M N Klaver, B K Mahmoodi, et al.High-density versus low-density mapping in ablation of atypical atrial flutter.Journal of Interventional Cardiac Electrophysiology, 2021, 62(3): 587-599. [26] A L Schwartz, E Chorin, T Mann, et al.Reconstruction of the left atrium for atrial fibrillation ablation using the machine learning CARTO 3 m-FAM software.Journal of Interventional Cardiac Electrophysiology, 2022, 64(1): 39-47. [27] B J Hansen, J Zhao, T A Csepe, et al.Atrial fibrillation driven by micro-anatomic intramural re-entry revealed by simultaneous sub-epicardial and sub-endocardial optical mapping in explanted human hearts.European Heart Journal, 2015, 36(35): 2390-2401. [28] J Zhao, B J Hansen, Y Wang, et al.Three-dimensional integrated functional, structural, and computational mapping to define the structural “Fingerprints” of heart-specific atrial fibrillation drivers in human heart ex vivo.Journal of the American Heart Association, 2017, 6(8): e005922. [29] Y Lou, X Zhou, W Mao.Electrocardiographic imaging: Insights for clinical practice.Journal of Electrocardiology and Circulation, 2021, 40(6): 559-564. [30] M P Ehrlich, G Laufer, I Coti, et al.Noninvasive mapping before surgical ablation for persistent, long-standing atrial fibrillation.The Journal of Thoracic and Cardiovascular Surgery, 2019, 157(1): 248-256. [31] C Ramanathan, R N Ghanem, P Jia, et al.Noninvasive electrocardiographic imaging for cardiac electrophysiology and arrhythmia.Nature Medicine, 2004, 10(4): 422-428. [32] F Plesinger, P Nejedly, I Viscor, et al.Parallel use of a convolutional neural network and bagged tree ensemble for the classification of Holter ECG.Physiological Measurement, 2018, 39(9): 094002. [33] A Rizwan, A Zoha, I B Mabrouk, et al.A review on the state of the art in atrial fibrillation detection enabled by machine learning.IEEE Reviews in Biomedical Engineering, 2021, 14: 219-239. [34] A Y Hannun, P Rajpurkar, M Haghpanahi, et al.Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network.Nature Medicine, 2019, 25(1): 65-69. [35] Z Bao, Y Bai, Q Huang, et al.Atrial fibrillation recognition algorithm based on CNN and BLSTM.Computer Engineering and Design, 2022, 43(5): 1312-1318. [36] R Zhao, P Xu, Y Liu.ECG-based atrial fibrillation detection based on deep convolutional residual neural network.Computer Science, 2022, 49(5): 186-193. [37] X Wen, Y Huang, X Wu, et al.A feasible feature extraction method for atrial fibrillation detection from BCG.IEEE Journal of Biomedical and Health Informatics, 2020, 24(4): 1093-1103. [38] H Dang, M Sun, G Zhang, et al.A novel deep arrhythmia-diagnosis network for atrial fibrillation classification using electrocardiogram signals.IEEE Access, 2019, 7: 75577-75590. [39] M Deng, L Qiu, H Wang, et al.Atrial fibrillation classification using convolutional neural networks and time domain features of ECG sequence. [40] M Limam, F Precioso.Atrial fibrillation detection and ECG classification based on convolutional recurrent neural network. [41] Y Liu, J Chen, B Fang, et al.Ensemble learning-based atrial fibrillation detection from single lead ECG wave for wireless body sensor network.IEEE Transactions on Network Science and Engineering, 2022: 1-10. DOI: 10.1109/TNSE.2022.3184523. [42] M S Islam, M N Islam, N Hashim, et al.New hybrid deep learning approach using BiGRU-BiLSTM and multilayered dilated CNN to detect arrhythmia.IEEE Access, 2022, 10: 58081-58096. [43] C McGann, N Akoum, A Patel, et al.Atrial fibrillation ablation outcome is predicted by left atrial remodeling on MRI.Circulation: Arrhythmia and Electrophysiology, 2014, 7(1): 23-30. [44] Q Xia, Y Yao, Z Hu, et al.Automatic 3D atrial segmentation from GE-MRIs using volumetric fully convolutional networks. [45] C Bian, X Yang, J Ma, et al.Pyramid network with online hard example mining for accurate left atrium segmentation. [46] S Vesal, N Ravikumar, A Maier.Dilated convolutions in neural networks for left atrial segmentation in 3D gadolinium enhanced-MRI. [47] C Li, Q Tong, X Liao, et al.Attention based hierarchical aggregation network for 3D left atrial segmentation. [48] É Puybareau, Z Zhao, Y Khoudli, et al.Left atrial segmentation in a few seconds using fully convolutional network and transfer learning. [49] J Chen, G Yang, Z Gao, et al.Multiview two-task recursive attention model for left atrium and atrial scars segmentation. [50] S Jia, A Despinasse, Z Wang, et al.Automatically segmenting the left atrium from cardiac images using successive 3D U-nets and a contour loss. [51] Y Liu, Y Dai, C Yan, et al.Deep learning based method for left atrial segmentation in GE-MRI. [52] D Borra, A Masci, L Esposito, et al.A semantic-wise convolutional neural network approach for 3-D left atrium segmentation from late gadolinium enhanced magnetic resonance imaging. [53] M F McGillivray, W Cheng, N S Peters, et al. Machine learning methods for locating re-entrant drivers from electrograms in a model of atrial fibrillation.Royal Society Open Science, 2018, 5(4): 172434. [54] A M Zolotarev, B J Hansen, E A Ivanova, et al.Optical mapping-validated machine learning improves atrial fibrillation driver detection by multi-electrode mapping.Circulation: Arrhythmia and Electrophysiology, 2020, 13(10): e008249. [55] M I Alhusseini, F Abuzaid, A J Rogers, et al.Machine learning to classify intracardiac electrical patterns during atrial fibrillation: Machine learning of atrial fibrillation.Circulation: Arrhythmia and Electrophysiology, 2020, 13(8): e008160. [56] G R Ríos-Muñoz, F Fernández-Avilés, Á Arenal. Convolutional neural networks for mechanistic driver detection in atrial fibrillation.International Journal of Molecular Sciences, 2022, 23(8): 4216. [57] S Liao, D Ragot, S Nayyar, et al.Deep learning classification of unipolar electrograms in human atrial fibrillation: Application in focal source mapping.Frontiers in Physiology, 2021, 12: 704122. [58] D Dobrev, T Dobreva, V Krasteva, et al.High-Q comb FIR filter for mains interference elimination.Annual Journal of Electronics, 2010, 4: 126-129. [59] J J Rieta, F Castells, C Sánchez, et al.Atrial activity extraction for atrial fibrillation analysis using blind source separation.IEEE Transactions on Bio-medical Engineering, 2004, 51(7): 1176-1186. [60] I I Christov, I K Daskalov.Filtering of electromyogram artifacts from the electrocardiogram.Medical Engineering & Physics, 1999, 21(10): 731-736. [61] J Joy, P Manimegalai.Wavelet based EMG artifact removal from ECG signal.Journal of Engineering Computers & Applied Sciences, 2013, 2(2319): 55-58. [62] J Chen, H Jiang, N Xu, et al.Motion sensor aided motion artifact recognition and removal method for ECG.Transducer and Microsystem Technologies, 2016, 35(1): 49-51, 55. [63] A Ghazarian, J Zheng, D Struppa, et al.Assessing the reidentification risks posed by deep learning algorithms applied to ECG data.IEEE Access, 2022, 10: 68711-68723. [64] P Huang, L Guo, M Li, et al.Practical privacy preserving ECG-based authentication for IoT-based healthcare.IEEE Internet of Things Journal, 2019, 6(5): 9200-9210. |
| No related articles found! |
| 阅读次数 | ||||||
|
全文 |
|
|||||
|
摘要 |
|
|||||