Signal analysis and classification methods for the calcium transient data of stem cell-derived cardiomyocytes

•Calcium cycling is important in cardiomyocytes and in cardiac functionality.•Data analysis techniques for stem cell derived cardiomyocytes were developed.•The techniques were based on peak detection and classification.•Signals were classified into normal or abnormal classes with machine learning.•It is crucial to detect abnormal signals corresponding to abnormal cells of culture.

Calcium cycling is crucial in the excitation–contraction coupling of cardiomyocytes, and therefore has a key role in cardiac functionality. Cardiac disorders and different drugs alter the calcium transients of cardiomyocytes and can cause serious dysfunction of the heart. New insights into this biochemical phenomena can be achieved by studying and analyzing calcium transients. Calcium transients of spontaneously beating human induced pluripotent stem cell-derived cardiomyocytes were recorded for a data set of 280 signals. Our objective was to develop and program procedures: (1) to automatically detect cycling peaks from signals and to classify the peaks of signals as either normal or abnormal, and (2) on the basis of the preceding peak detection results, to classify the entire signals into either a normal class or an abnormal class. We obtained a classification accuracy of approximately 80% compared to class decisions made separately by an experienced researcher, which is promising for the further development of an automatic classification approach. Automated classification software would be beneficial in the future for analyzing cardiomyocyte functionality on a large scale when screening for the adverse cardiac effects of new potential compounds, and also in future clinical applications.