## 4 Discussion  
Our VF detection and VF episode prediction results were compared with other results from the specialized literature. Thus, in Table 8, these results are presented and compared with those obtained by us. It is found that both in the case of VF detection and in the case of VF prediction, the results of the method proposed by us are better or at least comparable. It depends on the classifier we use. Thus, the best results which are superior to the other methods are those obtained with ensembles of classifiers. The robustness of the presented method, that is, of the segmentation and with features extracted and used in the case of VF detection or prediction, is demonstrated by the very good results obtained with most of the tested classifiers. For a fair comparison, the results of the methods that were tested on ECG recordings that also came from the MIT-BIH database were retrieved. Thus, in Table 8, the results presented are those of some methods that used the same databases as those used by us. The advantage of the method proposed by us is that very good VF detection results are obtained even from the first 3 s of its installation. For the prediction part, the advantage of the method is that the method is able to predict the installation of a VF 40 or 50 min in advance. In the methods presented in Table 8, there are generally methods that use features related to those used by us or similar classifiers or anticipation/detection time windows similar to ours. Using a Boltzmann network and time-frequency features extracted from the preprocessed ECG signal, the authors of the article [1] detect ventricular fibrillation. In paper [2], the possibility of establishing a set of parameters was analyzed to ensure both the reliable detection of shock-susceptible rhythms and an adequate prediction of shock success. A set of 10 features was used, reflecting the frequency characteristics, variations, complexity, periodicity and symmetry of the ECG signals. In paper [3], an algorithm for VF/VT detection is proposed using a band-pass digital filter with integer coefficients, which is very simple to implement in real-time operation. A sensitivity of 95.93% and a specificity of 94.38% were obtained. Using the same database as us, the authors of article [5] use approximate entropy, obtaining classification results (Acc = 91.0%) close to those obtained by us with MLP (91.8%) and lower than ours obtained with an ensemble of classifiers (94.6%). Using a reduced data set and approximate entropy with Empirical Mode Decomposition (EMD), the authors of article [6] report a classification of 91.2%. Very good results (Acc = 96.3%) were also reported in [16] when an SVM and a 2 s window was used, but with the mention that the signals come from different sources, \begin{table}
\begin{tabular}{c c c} \hline \hline
**MLP Configurations** & **Learning Rate** & **Classification Rate** \\ \hline \multicolumn{3}{c}{**MLP with hypermathematical optimization technique**} \\ \hline
26–25–1 & 0.00382 & 86.3\% \\
26–33–1 & 0.00115 & 87.3\% \\
26–30–1 & 0.99815 & 87.1\% \\ \hline \multicolumn{3}{c}{**MLP without hypermathematical optimization technique**} \\ \hline
25–10–1 & default & 87.3\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results with MLP for classifications with 50 min before the onset of VF and 50 min after the end of VF for forecast (i.e., only data from SCDH database). namely, AHA and MYTH-BIH. In [8], using a type-2 fuzzy logic-based classifier for a three class multiclass classification (VF, VT and Normal) obtained good accuracy ratios (Acc(VF) = 90.9%). In [12], using a number of 17 traits, results of Acc = 94.79% were obtained when distinguishing between VF category and non-VF category. In [13], there were 13 parameters used, accounting for temporal, spectral, and complexity features of the ECG signal, using an SVM to distinguish between VF and non-VF categories with Sens = 95% and Spe = 99%.