Characterizing arrhythmia using machine learning analysis of Ca²⁺ cycling in human cardiomyocytes.

ABSTRACT: Accurate modeling of the heart electrophysiology to predict arrhythmia susceptibility remains a challenge. Current electrophysiological analyses are hypothesis-driven models drawing conclusions from changes in a small subset of electrophysiological parameters because of the difficulty of handling and understanding large datasets. Thus, we develop a framework to train machine learning classifiers to distinguish between healthy and arrhythmic cardiomyocytes using their calcium cycling properties. By training machine learning classifiers on a generated dataset containing a total of 3,003 healthy derived cardiomyocytes and their various arrhythmic states, the multi-class models achieved >90% accuracy in predicting arrhythmia presence and type. We also demonstrate that a binary classifier train

SUBMITTER: Pang JKS 

PROVIDER: S-EPMC9391413 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature