Cardiac Electrophysiology Studies Based on Image and Machine Learning

Yashbir Singh¹, Deepa¹, Shi-Yi Wu¹, Michael Friebe², João Manuel R. S. Tavares³ and Weichih Hu^1,

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

Cite this paper:
Yashbir Singh, Deepa, Shi-Yi Wu, Michael Friebe, João Manuel R. S. Tavares and 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

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.

Keywords:
cardiac electrophysiology cardiac re-modelling cardiac imaging machine learning

This work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

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