Characterizing arrhythmia using machine learning analysis of Ca2+ cycling in human cardiomyocytes

Figure 1. Framework to train machine learning algorithms to predict arrhythmogenesis and identify electrophysiological changes (A) Single-cell CM Ca2+ cycling is tracked and processed to obtain the electrophysiological data contained within experimental groups containing specific arrhythmic forms. (B) For a multi-class arrhythmia prediction model, the pooled dataset is split into multiple train-test splits to train 4 different ML models over 100 bootstraps to identify the best consistently performing ML classifier. (C) Binary classification models are trained using individual experiment groups containing a healthy CM class and the arrhythmic CM subclass of interest. Assessing the feature importance metric of the trained classifiers can deduce the biological implications of the arrhythmic subclass studied to enable targeted therapy.

Figure 2. Functional validation of GCaMP6s Ca2+ reporter by breaking down of Ca2+ cycling readout into biological parameters (A) Single frame of a video tracking BJ-CM Ca2+ cycling. The arrow marks a single CM ROI tracked. Scale bar, 50 μm. (B) Fluorescence over time plot of a single CM. Identification of fluorescence peaks such as (1) was automated using the three criteria described. (C) Annotation of individual Ca2+ transient parameters. Each parameter is computed for every detected Ca2+ transient peak and summarized across the entire Ca2+ cycling readout from a single CM video recording. (D) Flowchart highlighting the process to generate the electrophysiological data representing the healthy and arrhythmic Ca2+ waveforms in a singular experimental group. Each CM is represented by either healthy or an arrhythmic subtype classification and 16 Ca2+ handling parameters.

Figure 3. Healthy and arrhythmic CMs could not be separated on a linear principal-component analysis (A) Pooled dataset containing the healthy classification and the six different arrhythmic subclasses from the three experimental groups used in this study. (B) First two PCs depicting the healthy and arrhythmic CMs and their associated datasets, accounting for 69.8% of variation present. Colors correspond to the arrhythmic/healthy classification. Symbols correspond to the associated datasets. (C) Overall workflow to identify the most consistent ML classifiers trained on the multi-class pooled dataset and the optimal train-test split to prevent overfitting.

Figure 4. ML algorithms used to build the multi-class prediction model performed well despite no distinct separation detected from principal-component analysis (A) Accuracy of all four trained multi-class classification models on the held-out testing datasets. Dashed lines, individual prediction strategy; solid lines, majority voting strategy. (B) Accuracy and kappa metrices focusing on the three better performing multi-class classification models on the held-out testing datasets. (C) Sensitivity, specificity, and F1 scores of the XGB multi-class classification models assessed using the held-out testing datasets. Left: individual prediction strategy. Right: majority voting strategy. (D) Confusion matrixes of the XGB models assessed using the held-out testing datasets. The horizontal axis is the true classification of the CM test data, while the vertical axis is the XGB model predicted classification. Prediction errors fall within the column of the true classifications. Left: individual prediction strategy. Right: majority voting strategy. Mean ± SD is plotted for n = 100 independent bootstraps for all figures.

Figure 5. Biological changes due to drug-induced arrhythmogenesis are captured by machine learning classifiers (A) Representative Ca2+ cycling plots depicting qualitative changes in Ca2+ transients. (B) Boxplots depicting quantitative comparison across treatment groups for beat-to-beat duration (BeatToBeat_M) and 30%, 60%, and 90% repolarization duration parameters (Repolarization30_M, Repolarization60_M, and Repolarization90_M, respectively). Each point represents a single CM. ∗p < 0.05 and ∗∗∗∗p < 0.0001; two-tailed Student’s t test. Ctrl,10 μM, 40 μM plotted with n = 178, 173, 169 CMs respectively. (C) Overall workflow to validate that XGB classifiers use biologically appropriate features to determine arrhythmogenesis. XGB classifiers were trained on the two drug treatment binary datasets on three train-test splits, and their feature importance was determined. (D) Performance metrices of the XGB binary classifiers assessed using the held-out testing datasets. Left: accuracy. middle: sensitivity. Right: specificity. Mean ± SD is plotted for n = 100 independent bootstraps for all figures. (E and F) Total count of all parameters that scored >20 on the feature importance metrics across all…

Figure 6. Secondary electrophysiological changes sufficiently recapitulate E4031 treatment arrhythmogenesis (A) Total count of all parameters that scored >20 on the feature importance metrics across XGB models trained with reduced input variables. Top: repolarization 30% and 60% durations excluded. Bottom: all repolarization duration features excluded. (B) Performance metrices of the XGB binary classifiers from the 60:40 train-test split compared with models trained with reduced input variables. Left: accuracy. Middle: sensitivity. Right: specificity. Mean ± SD is plotted (n = 100 independent bootstraps).