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.
Highlights
The human cardiovascular system is the naturally specialized system which circulates oxygenated blood to the other body parts and collects deoxygenated blood at the same time
Modern cardiac electrophysiology studies include catheter-based arrhythmia ablation and transvenous device implantation, which are highly dependent on accurate, real-time cardiac imaging and machine learning [4]
Most reports suggest that machine learning and cardiac imaging has become effective technique at this time
Summary
The human cardiovascular system is the naturally specialized system which circulates oxygenated blood to the other body parts and collects deoxygenated blood at the same time. It is seen that arrhythmia occurs by the formation of the combination of pro arrhythmic substrate, triggers, and modulators To curb this problem, prior precaution and diagnosis are necessary, yet the human cardiac electrical system is not a plain-sailing task. To develop cost-effective real-time assessment techniques to diagnose the cardio electrophysiology pathologies can assist in disseminating the error-free information of the affected area of the patient for the further treatment procedure. Modern cardiac electrophysiology studies include catheter-based arrhythmia ablation and transvenous device implantation, which are highly dependent on accurate, real-time cardiac imaging and machine learning [4]. This era has been led to an explosion of advance technology especially machine learning. We tried to describe the already present techniques of imaging and what can be possible in the future in respect of machine learning with the cardiac image
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