Stem cells have been a hot topic in the cardiology community for the last decade and a half. Ever since we learned how to differentiate cardiomyocytes from embryonic and induced pluripotent stem cells, there has been a lot of research devoted to the potential of utilizing these cardiomyocytes for regenerative medicine, drug model studies, and arrhythmogenesis analysis. However, while cardiomyocyte purification methods have advanced significantly, methods for the identification and isolation of specific types of cardiomyocytes, such as ventricular or pacemaking cells, have not seen the same progress. Among the different avenues for accomplishing this task, the electrophysiological one is of particular interest because every cardiomyocyte type generates a distinct signature known as an action potential. The current standard for analyzing the action potential of a cardiomyocyte is an expert-level subjective thresholding of specific features, such as action potential duration. However this approach does not transfer across datasets and does not scale with the increasing populations of cardiomyocytes.
In this thesis, ideas from the machine learning and shape analysis communities are explored to develop new, automated methods for the analysis of cardiomyocytes based on their action potentials. These methods allow us to identify subpopulations of similar cardiomyocytes based on their action potential morphology, hypothesize the eventual chamber-specific fate of newly differentiated cardiomyocytes, and make effective comparisons between cardiomyocytes in drug and cell-line studies. The objective, scalable methods presented in this thesis present a new paradigm in performing analysis in high-throughput applications of cardiomyocytes via action potential morphology, and could be of large benefit to the cardiology and biology communities
