###### Abstract
When a rhythm originates from above the ventricles in the atria, it is termed a supraventricular (SVB) rhythm (narrow QRS or \(<\)120 ms), and when a rhythm originates from within the ventricles, it is termed a ventricular (VB) rhythm (broad QRS or \(>\)120 ms); these characteristics are represented in Figure 1.  
The Association for the Advancement of Medical Instrumentation (AAMI) provides guidelines to classify heartbeats in an ECG signal into normal beats (N), supraventricular beats (SVB), ventricular beats (VB), fusion beats (FB), and unclassified beats (QB) [16; 17]. However, because of their rarity in patients, the last two types of heartbeats are not usually included, and both QB and FB are relabeled simply as VB, leaving the task to classify only between NB, SVB, and VB. An issue is the way research approaches the use of data sets. There are two accepted approaches to the problem, namely, inter- and intra-patient methodologies [18]. Works related to the inter-patient paradigm have lower performance against intra-patient works because of the use of different patients' heartbeats in training, validation, and testing sets. However such works reflect the reality accurately since the inevitable variabilities among patients are incorporated in their results. In this research, we present a review of previous work wherein it is shown that until nowm it has not been possible to overcome the variabilities among patients and train a generalized model from the databases available. The guidance of a physician is still needed to develop a patient adaptive model. This means that in the future, physicians would need to label new data because the signal for every individual patient changes in shape and heart rate, and the old parameters of the algorithm may need to be updated. In this article, a novel unsupervised method is introduced that classifies between beats of supraventricular origin (SVBo), formed by the NB and SVB classes, and beats of ventricular origin (VBo), formed by the VB and FB. This approach does not require the assistance of a physician since it tries to capture the inherent patterns in the signal, and it uses heartbeat features from previous works across patients. Based on our experiments, it was concluded that this methodology works best when they are at least 40 VBo. The present approach may be used as a first step to distinguish between the remaining NB and SVB in future experiments. The silhouette coefficient was implemented in MATLAB and used at the beginning to evaluate the quality of the clustering based on some parameters. For the experiments, an algorithm for clustering was implemented in MATLAB, and since this is an adaptive model for self-organizing maps, meaning that we could not use the silhouette score in general, and we would have to use it for every particular case or patient, which would yield just a distribution with no information worth for our purposes, as explained in Section 3.7, we chose between some parameters from the outcome that we derived from the comparison against the labels. In a future study with a modified algorithm, we aim to use the silhouette coefficient for the fine-tuning of the algorithm and better parameters.