## 4 Discussion
In [14], using six features from the frequency domain, nonlinear characteristics domain and using binary decision tree or the SVM, Acc = 94.2% and Acc = 89.3% were obtained, respectively. In [15], using digital Taylor-Fourier transform (DTFT) features and a least square support vector machine (LS-SVM) with linear and radial basis function (RBF), kernels obtained performance values of Acc = 83.75%. In paper [20], using boosted classification and regression tree (Boosted-CART) on six features for a binary VFV and non-VFVT classification, a classification of Acc = 98.29% was obtained. To test the method proposed by the authors, three databases were used (Creighton University Ventricular Tachyarrhythmia Database--CUDB, the MIT-BIH Malignant Ventricular Arrhythmia Database--VFDB and the MITBIH arrhythmia database--MITDB) totaling 1888 VT/VF samples and 27,992 non-VT/VF samples. Thus, the good classification results can be explained by the advantage offered by a very large database used for training. Another major difference is the size of the signal window, which in the case of the article [20] is 5 s, compared to our segmentation which is 3 s.  
In paper [21], two input data to the classifier are evaluated: TDA features and Persistence Diagram Image (PDI). Using the reduced TDA-obtained features, a high average \begin{table}
\begin{tabular}{l c c c} \hline \hline
**The Method** & **Sens \%** & **Spec \%** & **Acc \%** & **Database** \\ \hline
**DETECTION of VF in this paper** & & & \\ \hline with MLP in 3 s & & 91.8\% & VFDB MIT-BIH \\ with ensemble of classifiers & & 94.6\% & VFDB MIT-BIH \\ With SVM & & 94.2\% & VFDB MIT-BIH \\ \hline
**PREDICTION of VF in this paper** & & & \\ with MLP\(\_\)20 min in advance & & 99.7\% & NSR \& SDCH MIT-BIH \\ with MLP\(\_\)30 min in advance & & 98.9\% & NSR \& SDCH MIT-BIH \\ with MLP\(\_\)40 min in advance & & 99.9\% & NSR \& SDCH MIT-BIH \\ with MLP\(\_\)50 min in advance & & 99.6\% & NSR \& SDCH MIT-BIH \\ with MLP\(\_\)40 min in advance & & 95.3\% & NSR \& SDCH MIT-BIH \\ with MLP\(\_\)50 min in advance & & 96.1\% & NSR \& SDCH MIT-BIH \\ with ensemble of classifiers—30 min in advance & & 86.7\% & SCDH MIT-BIH \\ with ensemble of classifiers—40 min in advance & & 88.3\% & SCDH MIT-BIH \\ with ensemble of classifiers—50 min in advance & & 90.3\% & SCDH MIT-BIH \\ with MLP with hyperparameters optimization & & & 87.3\% & SCDH MIT-BIH \\ \hline using Boltzmann [1] & 92.52 & & & MIT-BIH \\ using Discriminant Analysis [2] & 94.10 & 93.80 & & AHA \& MIT-BIH \\ using Filter and Counts [3] & 94.40 & 95.90 & 94.70 & AHA \& MIT-BIH \\ using Approximate Entropy [5] & 91.84 & 90.20 & 91.00 & MIT-BIH \\ using EMD \& App Entropy [6] & 90.47 & 91.66 & 91.20 & MIT-BIH \\ using KNN [7] & 98.10 & 88.00 & 93.20 & MIT-BIH \\ using RBF [7] & 91.53 & 90.91 & 91.30 & MIT-BIH \\ using Fuzzy [8] & & & 90.90 & MIT-BIH \\ using TSK Fuzzy [9] & & & 93.30 & MIT-BIH \\ using Mamdani Fuzzy [9] & & & 86.60 & MIT-BIH \\ using Random Forest Classifier [12] & 95.04 & 94.78 & 94.79 & CU \& MIT-BIH \\ using SVM [13] & 95.00 & 99.00 & & CU \& MIT-BIH \\ using Binary Decision Tree [14] & 95.30 & 94.50 & 94.20 & CU \& MIT-BIH \\ using SVM [14] & 90.40 & 91.60 & 89.30 & CU \& MIT-BIH \\ using LS-SVM with RBF kernels [15] & 85.20 & 82.46 & 83.75 & CU \& MIT-BIH \\ using Lempel-Ziv and EMD [19] & 98.15 & 96.01 & 97.10 & CU \& MIT-BIH \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Assessment of VF identification methods for analogy with our results (with gray background). accuracy near 99% was observed when discriminating four types of rhythms and specificity values higher than 97.16% in all cases. The very good results are due to the feature selection block used, namely, the Sequential Forward Selection (SFS) method. Thus, from 79 initial features, a total of 27 features from all representations (time domain, RPC, PDR, LR, and SR) were selected. This selection improves computational efficiency and reduces the model's generalization error by removing irrelevant features or noise.