## 5 Discussion  
One of the main differences among all works is the percentage of \(P^{+}\) in VBo, where our approach produces percentages above 90%, with the exception of the SUPRA database with 0.8930%. This means that a great number of beats classified as VBo are indeed VBo. The F-score of this work outperforms the general classifier models. [PERSON] et al. [23] and [PERSON] et al. [22] present the lowest F-score because of their \(P^{+}\) around 0.60, plunging the F-score value, while [PERSON] et al. [19] and [PERSON] et al. [20] maintain similar values for both \(S\) and \(P^{+}\), with F-score of 0.79 and 0.83 respectively. The inevitable variability between patients lowers the performance of these general classifiers. The results for this work are higher, with 0.88, because it presents a \(P^{+}\) near 100%. An additional comparison is made with works where they present models that can be adapted to a patient. The results to be compared are the models before physician's help is provided for the adaptive algorithms, otherwise, their results are nearly 95%. Only [PERSON] et al. [26] and [PERSON] et al. [27] presented this type of results, and [PERSON] et al. [24] presented the performance of the HAMILTON software on the MITBIH database for comparative results. As in the general models, the performance on the F-score for VBo in the algorithm proposed was superior over these methods. No patterns in their results exist showing misclassification measurements, which vary depending on the database. For example, [PERSON] presents balanced results in the MIIBIH database, while a very low 0.54 in \(P^{+}\) is presented in the SUPRA database, and in the INCART database, the \(P^{+}\) is 0.96 against a 0.88 for \(S\). Meanwhile, [PERSON] et al. [27] have very similar results with the HAMILTON software, with a low performance of 0.79% for the MITBIH database in \(P^{+}\), 0.93 for the SUPRA database for the same measurement, and very lower, 0.37, in the INCART database. Our approach has higher performance because it maintains an \(S\) always above 0.9 for \(P^{+}\) in every database; in this way, the derived F-score presents a superior performance. Overall, our approach has produced better results in F-score for VBo, compared to these studies. The physicians' need for constant monitoring of the arrhythmias presented the opportunity for the creation of algorithms or methodologies that perform automatic measurements of arrhythmia. Different approaches have been used by the scientific community, such as the intra- and inter-patient paradigms. Such approaches have historically employed manual data tagging by an expert. The works of [PERSON] et al. [20] and [PERSON] et al. [27] were the first to show results from prelabeled data. Those studies showed progress, compared to previous studies, but they maintained the limitation of not being able to overcome the variations between patients or to train a general model that uses the available databases, as explained in Section 3.1. Our work introduces a new unsupervised method that classifies beats of SVBo origin, formed by classes NB and SVB, and beats of ventricular origin VBo, formed by VB and FB. This method does not require the assistance of an expert physician, as it captures the inherent patterns found in the captured signal and makes use of the heartbeat characteristics captured from previous patient studies. Previous works need labels in order to create a heartbeat classifier. A general classifier, created by heartbeat samples from other patients, may not be the best solution as the inevitable variability lowers the performance of the classifier. Even though the adaptive patient classifiers are a promising solution in classifying arrhythmias, it needs a physician to identify a certain number of heartbeats to feed the algorithm for every patient. In this work, a new unsupervised algorithm was proposed to classify between supraventricular and ventricular heartbeats in a single patient, in a window time, without previous labels provided by a physician.