## 3 Results  
The synthetically generated physiologic baseline ECG trace is shown in Figure 1.  
When the amplitude change was in the range of 5-10% of the superimposed jitter (0.01 mV-0.02 mV), all models failed to classify the different beats with an acceptable accuracy (Figure 2). Figure 1: Synthetic physiologic baseline ECG trace generated using standardized McSharry algorithm. mV, millivol; ms, millisecond; P, P wave (depolarization atria); QRS, Q, R, S waves (depolarization ventricles); T, T wave (repolarization ventricles). However, with an increase in the differences in the amplitude of the respective ECG components of 0.04 mV and more, the accuracies of RF, GBM, and ensemble learning methods in detecting differences in the ECG pattern compared to the baseline ECG increased and reached discriminatory accuracies of \(\geq\)0.8 for all components of the ECG beat. However, the ability of all machine learning algorithms to detect changes in the modified ECG trace compared to the baseline ECG progressively decreased when sequentially increasing the identical Gaussian jitter from 0.1 mV to 0.3 mV. Figure 2: Accuracies of machine learning algorithms in difference detection of ECG patterns using three increasing identical Gaussian jitters (0.1 mV, 0.2 mV, 0.3 mV). ACC, accuracy; mV, millivolt. Nevertheless, the accuracy of RF, GBM, and ensemble learning methods in detecting occurring differences in the P wave (reflecting depolarization of the atria) and the T wave (reflecting repolarization of the ventricles) remained at a constant level, ranging from 0.8 to \(\geq\)0.9 irrespective of the Gaussian jitter applied at an amplitude difference of 0.04 mV or more (Figure 2). The Boruta algorithm indicated that the features with the highest influence were the components of the ECG trace where the amplitude was artificially increased (Figure 3). No other parts of the ECG signal played a relevant role. When investigating the potential of the different machine learning algorithms in a pathologic control ECG trace depicting ST segment depressions, we found that both RFs and GBMs demonstrated excellent accuracies of \(\geq\)0.98 in detecting changes in the ST segment (1.5 mm versus 2.0 mm ST segment depression) when a Gaussian jitter of 0.1 mV was superimposed. When sequentially increasing the identical jitter to 0.3 mV, the discriminatory power of both RFs and GBMs decreased, but remained high, with stable accuracies >0.96 (Figure 4A). Again, the Boruta algorithm indicated that the features with the highest influence were the components of the ECG trace where the amplitude was artificially modified (Figure 5). No other parts of the ECG besides the ST segment signal played a relevant role. Similar results were found when investigating the ECG trace of Long-QT syndrome (LQTS). Again, both RFs and GBMs demonstrated their discriminatory power for detecting a prolongation of QT interval (442 ms versus 450 ms) in the synthetic  
Figure 3: Comparison of feature importances (indicated by dense, black area under the curve) in ECG pattern differentiation using Boruta feature selection algorithm. P, Q, R, S, and T waves at a fixed difference from baseline ECG of 0.06 mV are depicted, corresponding to a sequentially increased Gaussian jitter (0.1 mV, 0.2 mV, 0.3 mV). Red curve represents the synthetic baseline ECG trace modified by the superimposed Gaussian jitter imitating artifacts. X-axis depicts time of ECG intervals in ms, and y-axis depicts amplitude of ECG signal in mV. ms, milliseconds; mV, millivolt; \(\Delta\), respective change from baseline ECG trace; P, P wave (depolarization atria); QRS, Q, R, S waves (depolarization ventricles); T, T wave (repolarization ventricles). ECG trace, reaching accuracies of 1.0 and \(\geq\)0.96, at a Gaussian jitter of 0.1 mV and 0.2 mV, respectively (Figure 4B).