## 2 Materials and Methods
### Machine Learning Algorithms Used  
Random forests (RFs), gradient boosting machines (GBMs), deep neural networks (DNNs), an ensemble learning technique, as well as logistic regressions as the standard comparator were applied to detect differences between the modified ECG beats and the baseline ECG trace. Random forests (RFs) are a type of supervised machine learning algorithm for both classification and regression tasks first proposed by [PERSON] [11]. The main principle of RFs is to construct several different, uncorrelated decision trees during the training phase and subsequently merge them together to create a single output. A random subset of features from a random subset of the dataset is generated using feature randomness, thereby aiding in constructing uncorrelated forests. Additionally, feature bagging or bootstrap aggregation can increase randomness and diversity of the dataset [17]. Advantages are its reduced risk of over-fitting, the ability to handle large datasets, the ability to provide both classification and regression tasks, as well as its ability to determine feature importances. However, RFs lack the ability to obtain an interpretation of causal links between predictors and response, thereby limiting their use in certain research fields [18]. Gradient boosting machines (GBMs) are an ensemble machine learning method derived by [PERSON] [19]. GBMs can be considered as an ensemble model of different, sequentially trained decision trees. The principle behind this algorithm is to consecutively fit new models to provide a more accurate estimate of the response variable and to construct new base-learners to be highly correlated with the negative gradient of the loss function, associated with the whole ensemble [20]. Nevertheless, GBMs are prone to over-fitting, require additional pre-processing of the data to ensure adequate model performance, require balanced datasets for classification tasks, and are associated with higher resource consumption, since extended training time for the training models is required. Deep neural networks (DNNs) are a type of deep learning (DL) machine learning method. DNNs are fully connected artificial neural networks (ANNs) consisting of input and output layers as well as multiple hidden layers in between. The input layer receives input data whilst the output layer performs the decision or prediction process regarding the input signal. The hidden layers of the artificial neural network are the defining part of the neural network architecture and allow the transformation of inputs, the approximation of functions, or the capturing of data patterns [21; 22]. DNNs can be supervised, unsupervised, or a combination of both. Deep learning methods can process vast amounts of features and are therefore useful when dealing with a large amount of data. They have been successfully integrated or could be practically applied in the future in several different fields ranging from robotics to natural language processing, from cybersecurity to speech recognition [21]. Nevertheless, due to both their weak statistical interpretability and poor reasoning regarding the achievement of their results and decisions, DL and DNNs are often considered \"black-box\" solutions. Ensemble learning methods are an alternative to deep learning methods. Ensemble learning refers to a concept that combines different machine learning models. Different baseline models (\"weak learners\") are trained to solve the same problem by using ensemble methods such as bagging, stacking, boosting, or averaging. Combining several individual models to improve prediction performance and outperformance of other machine learning models is the main advantage of ensemble learning methods [23]. Logistic regression analysis is a statistical model that is used to test the association of both categorical and continuous independent variables with a single dichotomous dependent variable [24]. Logistic regression analysis is based on a sigmoid function.