Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.stemcr.2022.06.005
Title: Characterizing arrhythmia using machine learning analysis of Ca2+cycling in human cardiomyocytes
Authors: Pang, Jeremy KS
Chia, Sabrina
Zhang, Jinqiu
Szyniarowski, Piotr
Stewart, Colin
Yang, Henry 
Chan, Woon-Khiong 
Ng, Shi Yan 
Soh, Boon-Seng 
Keywords: Science & Technology
Life Sciences & Biomedicine
Cell & Tissue Engineering
Cell Biology
SUDDEN CARDIAC DEATH
PROGERIA-SYNDROME
DYSFUNCTION
DEFECTS
Issue Date: 9-Aug-2022
Publisher: CELL PRESS
Citation: Pang, Jeremy KS, Chia, Sabrina, Zhang, Jinqiu, Szyniarowski, Piotr, Stewart, Colin, Yang, Henry, Chan, Woon-Khiong, Ng, Shi Yan, Soh, Boon-Seng (2022-08-09). Characterizing arrhythmia using machine learning analysis of Ca2+cycling in human cardiomyocytes. STEM CELL REPORTS 17 (8) : 1810-1823. ScholarBank@NUS Repository. https://doi.org/10.1016/j.stemcr.2022.06.005
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 trained to distinguish cardiotoxic arrhythmia from healthy electrophysiology could determine the key biological changes associated with that specific arrhythmia. Therefore, machine learning algorithms can be used to characterize underlying arrhythmic patterns in samples to improve in vitro preclinical models and complement current in vivo systems.
Source Title: STEM CELL REPORTS
URI: https://scholarbank.nus.edu.sg/handle/10635/242577
ISSN: 2213-6711
DOI: 10.1016/j.stemcr.2022.06.005
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