## 3 Results
### Results for Forecast of Ventricular Fibrillation
Figure 7: Results for various classifiers in the 13 classes and the confusion matrix for classification in 13 classes with SVM (Recall global = 37.93%; Specificity global = 98.77%; Error Rate global = 21.33%; Precision global = 82.88%; F1 Score global = 52.25%). Figure 8 presents the classification results from 40 min in advance of the time of FV installation with all classifiers accessible in MATLAB(r). All classifiers demonstrated good results of over 88%, with the best results exceeding 99% coming from an ensemble of classifiers. We achieved excellent results of over 95% for both 40 min and 50 min advance predictions using a Multi-Layer Perceptron (MLP) with a hidden layer consisting of 10 neurons and with 26 neurons on the input layer [36]. In Table 5, which presents the confusion matrix, it is found that the classification rate is approximately the same for each of the two classes. The classification rate with the MLP network is 95.3% for 40 min in advance and 96.1% for 50 min in advance compared to VF. For a more thorough analysis, we conducted predictions using ECG recordings obtained exclusively from the SCDH database. In order to establish a category of \"normal\" segments--segments that do not indicate a ventricular fibrillation (VF) event--we extracted 3 min intervals occurring 50 min after the onset of the VF episode, assuming the VF episode  
Figure 8: Results for various classifiers from MATLAB® environment in the 2 classes and confusion matrix for 2-class classification (VF vs. another class) for classification 40 min in advance of the time of FV installation. \begin{table}
\begin{tabular}{c|c|c|c|c} \hline \hline \multicolumn{2}{c|}{40 min before VF} & \multicolumn{2}{c|}{**NSR and SCDH**} & \multicolumn{2}{c}{**Predicted class**} \\ \cline{2-5} \multicolumn{2}{c|}{Predicted class (**MLP = 95.3\%**)} & & & **Normal** & **VF** \\ \cline{2-5} \multicolumn{2}{c|}{Recall = 94.29\%; Specificity \(\Rightarrow\) 96.39\%; Error Rate = 4.68\%; Precision = 95.91\%; F1 score = 95.28\%} & \multicolumn{2}{c|}{**True**} & \multicolumn{1}{c|}{**Normal**} & **305** & 13 \\ \cline{2-5  
had concluded by then. The selection of a 50 min interval was based on empirical data. However, it is crucial to acknowledge that the less favorable outcomes depicted in Figure 9 should be interpreted with caution due to our inability to accurately label the segments occurring 50 min post-VF event. There remains a possibility that some of these subjects experienced VF recurrence later, notwithstanding our classification of these segments as normal [36]. Moreover, this analysis was also carried out by segmenting 30, 40 and 50 min, respectively, in advance (Table 6). For the case \"50 min before the episode of VF vs. 50 min after the VF episode\", the confusion matrix for an ensemble of classifiers (type bagged trees) is presented in Figure 9. A very close rate is found for the two classes with a total classification rate of 90.3%. For the same data, i.e., only data from the SCDH database, we tested an MLP-type neural network with a predefined number of neurons on the hidden layer. We compared the results with a neural network in which the number of neurons on the hidden layer and