A Common-Ground Review of the Potential for Machine Learning Approaches in Electrocardiographic Imaging Based on Probabilistic Graphical Models

摘要: Machine learning (ML) methods have seen an explosion in their development and application. They are increasingly being used many different fields with considerable success. However, although the interest is growing, impact field of electrocardiographic imaging (ECGI) remains limited. One main reasons that ML has yet to become more prevalent ECGI published literature scattered there no common ground description comparison these MLframework. Here we address this limitation a review from perspective ML. We will use probabilistic modeling provide framework compare well known approaches. Finally, discuss which approaches been do inference on models alternatives could be utilized as mature.