A deep learning algorithm to translate and classify cardiac electrophysiology

Author:

Aghasafari Parya1,Yang Pei-Chi1,Kernik Divya C2,Sakamoto Kazuho3,Kanda Yasunari4ORCID,Kurokawa Junko3,Vorobyov Igor15ORCID,Clancy Colleen E1ORCID

Affiliation:

1. Department of Physiology and Membrane Biology, University of California, Davis, Davis, United States

2. Washington University in St. Louis, St. Louis, United States

3. Department of Bio-Informational Pharmacology, School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan

4. Division of Pharmacology, National Institute of Health Sciences, Kanagawa, Japan

5. Department of Pharmacology, University of California, Davis, Davis, United States

Abstract

The development of induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) has been a critical in vitro advance in the study of patient-specific physiology, pathophysiology, and pharmacology. We designed a new deep learning multitask network approach intended to address the low throughput, high variability, and immature phenotype of the iPSC-CM platform. The rationale for combining translation and classification tasks is because the most likely application of the deep learning technology we describe here is to translate iPSC-CMs following application of a perturbation. The deep learning network was trained using simulated action potential (AP) data and applied to classify cells into the drug-free and drugged categories and to predict the impact of electrophysiological perturbation across the continuum of aging from the immature iPSC-CMs to the adult ventricular myocytes. The phase of the AP extremely sensitive to perturbation due to a steep rise of the membrane resistance was found to contain the key information required for successful network multitasking. We also demonstrated successful translation of both experimental and simulated iPSC-CM AP data validating our network by prediction of experimental drug-induced effects on adult cardiomyocyte APs by the latter.

Funder

NIH

American Heart Association

National Heart, Lung, and Blood Institute

UC Davis Department of Physiology and Membrane Biology

National Science Foundation

National Centre for Supercomputing Applications

Texas Advanced Computing Center

Oracle

Publisher

eLife Sciences Publications, Ltd

Subject

General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,General Medicine,General Neuroscience

Reference82 articles.

1. Multitask-network;Aghasafari,2021

2. Machine learning to classify intracardiac electrical patterns during atrial fibrillation: machine learning of atrial fibrillation;Alhusseini;Circulation. Arrhythmia and Electrophysiology,2020

3. DeepHeart: semi-supervised sequence learning for cardiovascular risk prediction;Ballinger,2018

4. An accurate lstm based video heart rate estimation method;Bian,2019

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3