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

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.