@article{jbet2018611,
author={{Singh, Yashbir and Deepa, D and Wu, Shi-Yi and Friebe, Michael and Tavares, Jo?o Manuel R. S. and Hu, Weichih},
title={Cardiac Electrophysiology Studies Based on Image and Machine Learning},
journal={Journal of Biomedical Engineering and Technology},
volume={6},
number={1},
pages={1--6},
year={2018},
url={http://pubs.sciepub.com/jbet/6/1/1},
issn={2373-1303},
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.},
doi={10.12691/jbet-6-1-1}
publisher={Science and Education Publishing}
}