## 4 Discussion  
In this theoretical, proof-of-concept study, we report the discriminatory accuracies of five different machine learning algorithms in detecting subtle changes in the morphology of synthetically generated ECG signals superimposed with different degrees of artifacts. Using this experimental approach, we found that machine learning algorithms were highly discriminative for classifying the amplitude of specific parts of the physiologic ECG, especially the P and T waves, even if the signal-to-noise ratio was in the range of 0.1. The best classification results were obtained by both RFs and GBMs, whereas logistic regression yielded the lowest discriminative power. To the best of our knowledge, this is the first study to demonstrate the feasibility of detecting minimal or imperceptible changes of single ECG traces superimposed by artifacts with the help of machine learning algorithms. Our approach differs from the usual data interpretation performed so far; instead of using a longer ECG registration, we identified single beats and aligned them by the R wave. Using this method, it was possible to interpret a time point of the ECG signal relative to the R wave as a feature for the machine learning algorithm without analyzing the signal by a convolution layer of a neural network. Machine learning tools, particularly RFs, GBMs, DNNs, and ensemble learning methods, are attracting more and more interest as a solution for problems in supervised learning algorithms in the medical field. During the last few years, these algorithms have become the method of choice for several different settings in supervised pattern recognition in datasets. Typically, they are used to train classification models, allowing separate samples of different classes based on different features and estimating which features were important for this classification [26]. For example, the RF algorithm has become very popular for providing two aspects important for data mining: (1) high prediction accuracy and (2) information on variable importance for classification. The prediction performance of RFs compares well to other classification algorithms such as support vector machines or artificial neural networks. The five algorithms that we have chosen for our study are known to work well in medical tabulated data. Our selection of potential algorithms that could be applied for the detection of subtle ECG changes is far from complete. Other machine learning tools have been proposed as well for medical data and could have been used for our data analysis as well [27]. For example, 1D convolutional neural networks, a feed-forward artificial neural network consisting of alternating convolutional and subsampling layers [28], may theoretically have resulted in higher and more consistent accuracies in detecting changes in ECG morphology. However, for this approach, a large amount of training data is necessary, which can clearly be avoided by our approach. Additionally, since two of our tested algorithms resulted in very good results regarding the discriminatory accuracy, we refrained from testing more potential algorithms, since we expected that the results might be similar. However, we therefore cannot rule out the idea that another machine learning algorithm might be even better at classifying ECGs. The results of our study imply certain possibilities for future application in clinical medicine. For example, our results demonstrate that RFs and GBMs can reliably differentiate ECG morphologies even when substantial artifacts are present. Despite the fact that it is well known that RFs and GBMs often work best for classification tasks of tabular, medical data, the exact and precise reasons why some machine learning methods and algorithms work better at medical classification tasks than others are very difficult to define [29]. We therefore cannot really explain the reasons why RFs and GBMs performed that well in our study.