## 3 Results
### Results for Forecast of Ventricular Fibrillation  
In the case of predicting VF episodes, the aim was to analyze the relevance of features extracted from ECG signal segments taken 20, 30, 40 or 50 min in advance of VF episodes compared to ECG signal segments without heart disease. At the same time, we tested several classifiers on the same data to validate the method and to check which the most suitable classifier is. The relevance of the extracted features actually represents the ability to predict episodes of VF, prediction made by classifying the examples into two classes, namely, the normal class vs. the class that anticipates a VF. Table 4 displays the outcomes obtained for different moments in time. Very good results are found for all of these predictions. The purpose of our study is not to predict the moment when a subject will make FV, but the goal is to warn her/him that in the next hour, it is possible to suffer FV. \begin{table}
\begin{tabular}{c|c|c|c|c} \hline \hline \multicolumn{2}{c|}{20 min before VF} & \multicolumn{3}{c}{Predicted class} \\ \cline{2-5} \multicolumn{2}{c|}{Predicted class **(Ensemble of classifiers 99.7\%)**} & \multicolumn{3}{c|}{Normal} & **VF** \\ \cline{2-5} \multicolumn{2}{c|}{Recall = 99.72\%; Specificity = 99.67; Error Rate = 0.26\%; Precision = 99.63\%; F1 score = 99.56\%} & **True** & Normal & **1076** & 4 \\ \cline{2-5} \multicolumn{2}{c|}{} & **Class** & VF & 2 & **1196** \\ \hline \multicolumn{5}{c|}{30 min before VF} & \multicolumn{3}{c|}{Predicted class} \\ \cline{2-5} \multicolumn{2}{c|}{Predicted class **(Ensemble of classifiers 99.9\%)**} & \multicolumn{3}{c|}{Normal} & **VF** \\ \cline{2-5} \multicolumn{2}{c|}{Predicted class **(Ensemble of classifiers 99.9\%)**} & \multicolumn{3}{c|}{Predicted class} \\ \cline{2-5} \multicolumn{2}{c|}{Predicted class **(Ensemble of classifiers 99.9\%)**} & \multicolumn{3}{c|}{Normal} & **VF** \\ \cline{2-5} \multicolumn{2}{c|}{Predicted class **(Ensemble of classifiers 99.9\%)**} & \multicolumn{3}{c|}{Normal} & **VF** \\ \cline{2-5} \multicolumn{2}{c|}{Predicted class **(Ensemble of classifiers 99.9\%)**} & \multicolumn{3}{c|}{Normal} & **VF** \\ \cline{2-5} \multicolumn{2}{c|}{Predicted class **(Ensemble of classifiers 99.6\%)**} & \multicolumn{3}{c|}{Predicted class} \\ \cline{2-5} \multicolumn{2}{c|}{Recall = 99.81\%; Specificity = 99.42\%; Error Rate = 0.39\%; Precision = 99.35\%; F1 score = 99.58\%} & **True** & Normal & **1073** & 7 \\ \cline{2-5} \multicolumn{2}{c|}{Predicted class **(Ensemble of classifiers 99.5\%)**} & \multicolumn{3}{c|}{Predicted class} \\ \cline{2-5} \multicolumn{2}{c|}{Predicted class **(Ensemble of classifiers 99.6\%)**} & \multicolumn{3}{c|}{Normal} & **VF** \\ \cline{2-5} \multicolumn{2}{c|}{Recall = 99.81\%; Specificity = 99.42\%; Error Rate = 0.39\%; Precision = 99.35\%; F1 score = 99.58\%} & **True** & Normal & **1073** & 7 \\ \cline{2-5} \multicolumn{2}{c|}{Predicted class **(Ensemble of classifiers 99.5\%)**} & \multicolumn{3}{c|}{Predicted class} \\ \cline{2-5} \multicolumn{2}{c|}{Predicted class **(Ensemble of classifiers 99.6\%)**} & \multicolumn{3}{c|}{Normal} & **VF** \\ \cline{2-5} \multicolumn{2}{c|}{Recall = 99.81\%; Specificity = 99.42\%; Error Rate = 0.39\%; Precision = 99.35\%; F1 score = 99.58\%} & **True** & Normal & **1073** & 7 \\ \cline{2-5} \multicolumn{2}{c|}{Predicted class **(Ensemble of classifiers 99.6\%)**} & \multicolumn{3}{c|}{Normal} & **VF** & 2 & **1198** \\ \cline{2-5} \multicolumn{2}{c|}{Predicted class **(Ensemble of classifiers 99.42\%; Error Rate = 0.39\%; Precision = 99.35\%; F1 score = 99.58\%} & **True** & Normal & **1073** & 7 \\ \cline{2-5} \multicolumn{2}{c|}{Predicted class **(Ensemble of classifiers 99.5\%)**} & \multicolumn{3}{c|}{Predicted class} & **VF** & 2 & **1198** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of the prediction of VF episodes for different moments in time (with 20, 30, 40 and 50 min before VF event for training and testing with data from NSR and SCDH databases). Green background indicates correct classification.