## 4 Discussion
Nevertheless, our approach might be promising and could be used to compare real-time to baseline ECG traces in different physiologic situations and settings, even when relevant noise is present in the ECG signal. The theoretical applicability is wide, ranging from integration into wearable cardiovascular monitoring devices to continuous ECG monitors in healthcare settings. Thereby, machine learning algorithms detecting pre-defined ECG trace changes could enable targeted screening for respective ECG changes, thus identifying distinct pathologies (e.g., increases in the amplitude of the T wave, as seen in hyperkalemia). As only changes in ECG signals that have been defined a priori can be detected when using this approach, using large databases which include physiological and expert-diagnosed, pathologic ECG traces could inform appropriately trained models to allow for early detection of ECG pathologies equivalent to the accuracy of an experienced examiner. Several limitations need to be considered when interpreting the results of our study. First, we used synthetically generated ECG signals instead of ECG traces derived from real patients. This limits the generalizability of our findings to the clinical setting. Furthermore, when using our approach with clinical ECG data, pre-processing of the ECG trace must be performed by removing a baseline wander and applying different filters to account for potential artifacts caused by respiration or body movements that could lead to ECG beat morphology changes without cardiac origin [30]. Baseline wander noise frequencies are typically in the range of 0-0.5 Hz [31]. Using pre-processing, baseline noise can be suppressed while clinically important information remains visible. It remains unknown if such an approach would have influenced our results. Next, we generated our ECG sweeps beat by beat. As a consequence, the main shape of every sweep does not change over time. Therefore, in a real-life scenario, the classification tasks we performed in our simplified model might be more difficult in a clinical setting. However, since we did not use real clinical data, we cannot guess how much this influences our results. Furthermore, our study did not investigate whether all the confounders associated with conventional ECG trace monitoring (e.g., internal and external artifacts, the low resolution of monitors used, the impossibility of analyzing changes of the signal obtained on the fly, etc.) can be efficiently controlled with the help of our approach. However, the goal of our study was to demonstrate that small, subtle changes in the ECG trace can be detected using the approach described. Our results prove that changes in ECG traces can be detected even in the presence of a disadvantageous signal-to-noise ratio. Whether other inaccuracies can be controlled by our approach cannot be sufficiently demonstrated based on the data available. Finally, we analyzed only one physiological baseline ECG trace as a proof-of-concept, and additionally, two pathological cases highlighting ECG morphologies. Our results do not answer whether the ability to detect subtle changes in ECG traces is preserved when different baseline ECG morphologies are used.