Methods Inf Med
DOI: 10.1055/s-0040-1701484
Original Article
Georg Thieme Verlag KG Stuttgart · New York

Separation of HCM and LQT Cardiac Diseases with Machine Learning of Ca2+ Transient Profiles

Henry Joutsijoki
1  Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland
,
Kirsi Penttinen
2  Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
,
Martti Juhola
1  Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland
,
Katriina Aalto-Setälä
2  Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
3  Heart Center, Tampere University Hospital, Tampere, Finland
› Author Affiliations
Funding The work of the second author was supported by Finnish Foundation for Cardiovascular Research and Maud Kuistila Memorial Foundation.
Further Information

Publication History

16 September 2019

22 December 2019

Publication Date:
20 February 2020 (online)

Abstract

Background Modeling human cardiac diseases with induced pluripotent stem cells not only enables to study disease pathophysiology and develop therapies but also, as we have previously showed, it can offer a tool for disease diagnostics. We previously observed that a few genetic cardiac diseases can be separated from each other and healthy controls by applying machine learning to Ca2+ transient signals measured from iPSC-derived cardiomyocytes (CMs).

Objectives For the current research, 419 hypertrophic cardiomyopathy (HCM) transient signals and 228 long QT syndrome (LQTS) transient signals were measured. HCM signals included data recorded from iPSC-CMs carrying either α-tropomyosin, i.e., TPM1 (HCMT) or MYBPC3 or myosin-binding protein C (HCMM) mutation and LQTS signals included data recorded from iPSC-CMs carrying potassium voltage-gated channel subfamily Q member 1 (KCNQ1) mutation (long QT syndrome 1 [LQT1]) or KCNH2 mutation (long QT syndrome 2 [LQT2]). The main objective was to study whether and how effectively HCMM and HCMT can be separated from each other as well as LQT1 from LQT2.

Methods After preprocessing those Ca2+ signals where we computed peak waveforms we then classified the two mutations of both disease pairs by using several different machine learning methods.

Results We obtained excellent classification accuracies of 89% for HCM and even 100% for LQT at their best.

Conclusion The results indicate that the methods applied would be efficient for the identification of these genetic cardiac diseases.