Methods of information in medicine · 2020
Separation of HCM and LQT Cardiac Diseases with Machine Learning of Ca2+ Transient Profiles.
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Joutsijoki Henry, Penttinen Kirsi, Juhola Martti, Aalto-Setälä Katriina
Аннотация
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.
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.