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.

中文翻译：

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用Ca2 +瞬态曲线的机器学习来分离HCM和LQT心脏病。

背景技术用诱导的多能干细胞对人类心脏疾病进行建模，不仅能够研究疾病的病理生理学并开发治疗方法，而且，正如我们之前所证明的那样，它还能为疾病诊断提供一种工具。我们以前观察到，通过将机器学习应用于从iPSC衍生的心肌细胞（CMs）测得的Ca2 +瞬时信号，可以将几种遗传性心脏病与健康对照区分开。目的本研究测量了419例肥厚型心肌病（HCM）瞬态信号和228例长QT综合征（LQTS）瞬态信号。HCM信号包括从携带i-原肌球蛋白的iPSC-CM记录的数据，即 TPM1（HCMT）或MYBPC3或肌球蛋白结合蛋白C（HCMM）突变和LQTS信号包括从iPSC-CM记录的数据，这些iPSC-CM携带钾电压门控性通道亚家族Q成员1（KCNQ1）突变（长QT综合征1 [LQT1]）或KCNH2突变（长QT综合征2 [LQT2]）。主要目标是研究HCMM和HCMT以及LQT1和LQT2是否可以彼此分离以及如何有效分离。方法在对那些我们计算出峰值波形的Ca2 +信号进行预处理之后，我们然后使用几种不同的机器学习方法对两个疾病对的两个突变进行分类。结果我们获得了HCM极好的分类精度，最好的LQT达到了100％。结论结果表明所采用的方法将是有效的这些遗传性心脏病的鉴定。