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常 益, 董 明, 王 彬, 范 力. [Developments of ex vivo cardiac electrical mapping and intelligent labeling of atrial fibrillation substrates]. SHENG WU YI XUE GONG CHENG XUE ZA ZHI = JOURNAL OF BIOMEDICAL ENGINEERING = SHENGWU YIXUE GONGCHENGXUE ZAZHI 2024; 41:184-190. [PMID: 38403620 PMCID: PMC10894749 DOI: 10.7507/1001-5515.202211046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Revised: 10/13/2023] [Indexed: 02/27/2024]
Abstract
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.
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Affiliation(s)
- 益 常
- 西安交通大学 电工材料电气绝缘国家重点实验室(西安 710049)State Key Library of Electrical Insulation and Power Equipment, Xi’an Jiaotong University, Xi’an 710049, P. R. China
| | - 明 董
- 西安交通大学 电工材料电气绝缘国家重点实验室(西安 710049)State Key Library of Electrical Insulation and Power Equipment, Xi’an Jiaotong University, Xi’an 710049, P. R. China
| | - 彬 王
- 西安交通大学 电工材料电气绝缘国家重点实验室(西安 710049)State Key Library of Electrical Insulation and Power Equipment, Xi’an Jiaotong University, Xi’an 710049, P. R. China
| | - 力宏 范
- 西安交通大学 电工材料电气绝缘国家重点实验室(西安 710049)State Key Library of Electrical Insulation and Power Equipment, Xi’an Jiaotong University, Xi’an 710049, P. R. China
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Hunt B, Kwan E, Tasdizen T, Bergquist J, Lange M, Orkild B, MacLeod RS, Dosdall DJ, Ranjan R. Transfer Learning for Improved Classification of Drivers in Atrial Fibrillation. COMPUTING IN CARDIOLOGY 2023; 50:10.22489/cinc.2023.412. [PMID: 38405161 PMCID: PMC10887411 DOI: 10.22489/cinc.2023.412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
"Drivers" are theorized mechanisms for persistent atrial fibrillation. Machine learning algorithms have been used to identify drivers, but the small size of current driver datasets limits their performance. We hypothesized that pretraining with unsupervised learning on a large dataset of unlabeled electrograms would improve classifier accuracy on a smaller driver dataset. In this study, we used a SimCLR-based framework to pretrain a residual neural network on a dataset of 113K unlabeled 64-electrode measurements and found weighted testing accuracy to improve over a non-pretrained network (78.6±3.9% vs 71.9±3.3%). This lays ground for development of superior driver detection algorithms and supports use of transfer learning for other datasets of endocardial electrograms.
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Affiliation(s)
- Bram Hunt
- Department of Biomedical Engineering, University of Utah, SLC, UT, USA
- Nora Eccles Cardiovascular Research and Training Institute, University of Utah, SLC, UT, USA
- Division of Cardiovascular Medicine, University of Utah, SLC, UT, USA
| | - Eugene Kwan
- Department of Biomedical Engineering, University of Utah, SLC, UT, USA
- Nora Eccles Cardiovascular Research and Training Institute, University of Utah, SLC, UT, USA
- Division of Cardiovascular Medicine, University of Utah, SLC, UT, USA
| | - Tolga Tasdizen
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, USA
- Department of Electrical and Computer Engineering, University of Utah, SLC, UT, USA
| | - Jake Bergquist
- Department of Biomedical Engineering, University of Utah, SLC, UT, USA
- Nora Eccles Cardiovascular Research and Training Institute, University of Utah, SLC, UT, USA
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, USA
| | - Matthias Lange
- Nora Eccles Cardiovascular Research and Training Institute, University of Utah, SLC, UT, USA
- Division of Cardiovascular Medicine, University of Utah, SLC, UT, USA
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, USA
| | - Benjamin Orkild
- Department of Biomedical Engineering, University of Utah, SLC, UT, USA
- Nora Eccles Cardiovascular Research and Training Institute, University of Utah, SLC, UT, USA
- Division of Cardiovascular Medicine, University of Utah, SLC, UT, USA
| | - Robert S MacLeod
- Department of Biomedical Engineering, University of Utah, SLC, UT, USA
- Nora Eccles Cardiovascular Research and Training Institute, University of Utah, SLC, UT, USA
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, USA
| | - Derek J Dosdall
- Department of Biomedical Engineering, University of Utah, SLC, UT, USA
- Nora Eccles Cardiovascular Research and Training Institute, University of Utah, SLC, UT, USA
- Division of Cardiovascular Medicine, University of Utah, SLC, UT, USA
- Division of Cardiothoracic Surgery, Department of Surgery, University of Utah, SLC, UT, USA
| | - Ravi Ranjan
- Department of Biomedical Engineering, University of Utah, SLC, UT, USA
- Nora Eccles Cardiovascular Research and Training Institute, University of Utah, SLC, UT, USA
- Division of Cardiovascular Medicine, University of Utah, SLC, UT, USA
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Cardiovascular Diseases in the Digital Health Era: A Translational Approach from the Lab to the Clinic. BIOTECH 2022; 11:biotech11030023. [PMID: 35892928 PMCID: PMC9326743 DOI: 10.3390/biotech11030023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 06/19/2022] [Accepted: 06/27/2022] [Indexed: 11/16/2022] Open
Abstract
Translational science has been introduced as the nexus among the scientific and the clinical field, which allows researchers to provide and demonstrate that the evidence-based research can connect the gaps present between basic and clinical levels. This type of research has played a major role in the field of cardiovascular diseases, where the main objective has been to identify and transfer potential treatments identified at preclinical stages into clinical practice. This transfer has been enhanced by the intromission of digital health solutions into both basic research and clinical scenarios. This review aimed to identify and summarize the most important translational advances in the last years in the cardiovascular field together with the potential challenges that still remain in basic research, clinical scenarios, and regulatory agencies.
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