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Motwani M, Dey D, Berman DS, Germano G, Achenbach S, Al-Mallah MH, Andreini D, Budoff MJ, Cademartiri F, Callister TQ, Chang HJ, Chinnaiyan K, Chow BJ, Cury RC, Delago A, Gomez M, Gransar H, Hadamitzky M, Hausleiter J, Hindoyan N, Feuchtner G, Kaufmann PA, Kim YJ, Leipsic J, Lin FY, Maffei E, Marques H, Pontone G, Raff G, Rubinshtein R, Shaw LJ, Stehli J, Villines TC, Dunning A, Min JK, Slomka PJ. Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis. Eur Heart J. 2017; 38: 500-507.

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Article

Cardiac Electrophysiology Studies Based on Image and Machine Learning

1Department of Biomedical Engineering, Chung Yuan Christian University, Zhongli, Taiwan

2Electrical Engineering and Information Technologies, Otto-von-Guericke-University, Magdeburg, Germany

3Instituto de Ciência e Inovação em Engenharia Mecânicae Engenharia Industrial, Departamento de Engenharia Mecânica, Faculdade de Engenharia, Universidade do Porto, Porto, PORTUGAL


Journal of Biomedical Engineering and Technology. 2018, Vol. 6 No. 1, 1-6
DOI: 10.12691/jbet-6-1-1
Copyright © 2018 Science and Education Publishing

Cite this paper:
Yashbir Singh, Deepa, Shi-Yi Wu, Michael Friebe, João Manuel R. S. Tavares, Weichih Hu. Cardiac Electrophysiology Studies Based on Image and Machine Learning. Journal of Biomedical Engineering and Technology. 2018; 6(1):1-6. doi: 10.12691/jbet-6-1-1.

Correspondence to: Weichih  Hu, Department of Biomedical Engineering, Chung Yuan Christian University, Zhongli, Taiwan. Email: weichihhu@cycu.edu.tw

Abstract

Cardiac Electrophysiology study is the origin and treatment of arrhythmia, which is an abnormality in the rate, regularity or sequence of cardiac activation. There has been considerable recent development in this field, where computational methods such as Imaging and Machine Learning for Cardiac Electrophysiology, provide the framework for cardiac re-modeling. In this research, we review various recent strategies currently available for the meeting the goal of structurally and functionally integrated models of cardiac function that combine data intensive cellular systems models with compute-intensive anatomically detailed multiscale simulations.

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