## 4 Results
The hyperparameters of each feature set presented the best \(F\) were as follows: feature set \(1=\
u=0.05\), \(\gamma=2.25\), \(d_{1}=0.45\), \(d_{2}=0.55\), \(d_{3}=0.35\); feature set 3 = \(\
u=0.1\), \(\gamma=1.25\), \(d_{1}=0.3\), \(d_{2}=0.55\), \(d_{3}=0.3\); feature set 4 = \(\
u=0.15\), \(\gamma=1\), \(d_{1}=0.35\), \(d_{2}=0.55\), \(d_{3}=0.3\). It has to be noticed that even though these set of features were selected with almost the same parameters values, they ended up with completely different and \(Fg\) values. The best set of features with its parameters was feature set 4, with 0.8220%. With this, the experimentation \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **SUPRA** & **MITBIH** & **INCART** & **F-General** \\ \hline \(2\times 2\) & 0.81866 & 0.86893 & 0.92047 & 0.75318503 \\ \(3\times 2\) & 0.84277 & 0.87458 & 0.91985 & 0.77126533 \\ \(3\times 3\) & 0.83095 & 0.86631 & 0.9041 & 0.75056012 \\ \(4\times 3\) & 0.7909 & 0.85177 & 0.89498 & 0.71276568 \\ \(4\times 4\) & 0.78168 & 0.85879 & 0.89839 & 0.71262883 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Selecting the Best of SOM Topology. \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **SUPRA** & **MITBIH** & **INCART** & **F-General** \\ \hline feature set 1 & 0.8892 & 0.8755 & 0.9214 & 0.801129807 \\ feature set 2 & 0.8602 & 0.8755 & 0.928 & 0.787117392 \\ feature set 3 & 0.8892 & 0.8755 & 0.9214 & 0.801129807 \\ feature & 0.8957 & 0.8723 & 0.9468 & 0.817466775 \\ feature & 0.8946 & 0.8721 & 0.8687 & 0.771506723 \\ feature & 0.8184 & 0.7725 & 0.9029 & 0.686694226 \\ feature set 7 & 0.8608 & 0.8721 & 0.8919 & 0.765261291 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Selecting the Best Three Features. concludes with a SOM network of \(3\times 2\) neurons, with feature set 4 and the parameters \(\
u=0.15\), \(\gamma=1\), \(d_{1}=0.35\), \(d_{2}=0.55\), \(d_{3}=0.3\). The results of all the databases are compared with similar works in Table 7. The approach was tested also with two databases derived from the MITBIH database, as in [21; 27]. Even though some other authors such as [PERSON] et al. [34] claim that the average detection accuracies of both VB and SPV ectopic beats were over 97% using the MIT-BIH arrhythmia database, for both training and testing of the algorithm, this shows that the supervised algorithms so far continue being of higher precision or accuracy. In Figure 8, the performance of the algorithm for 109 records that have at least 40 VBos is shown. The histogram shows that 89 out of 109 records have at least a 0.85 F-score, and 60 out of 109 are over 0.95 F-score. These records are 30 min long; in other words, there are approximately 1500 and up to 2500 heartbeats depending on the heart rate. These records represent most of the VBos in the three databases. \begin{table}
\begin{tabular}{l l l l l} \hline  & **SUPRA** & **MITBIH** & **INCART** & **F-General** \\ \hline feature & 0.9005 & 0.8655 & 0.9276 & 0.80519243 \\ set 1 & 0.8892 & 0.8862 & 0.9187 & 0.80614369 \\ feature & 0.89569357 & 0.8862 & 0.9394 & 0.8220299 \\ set 4 & & & & \\ \hline \end{tabular}
\end{table} 
Table 6: Example of ECG signal separated in clusters and its descriptors. Figure 8: F-score performance with at least 40 VoB.