## 3 Results  
### Feature Selection Results  
The proposed features were divided into two groups according to whether or not they contained AF. They were then tested to determine their origin from the same population. For this purpose, we used the Mann-Whitney U-test, also known as the Wilcoxon rank sum test, which is a non-parametric statistical test for comparing two samples or groups. This verified whether the groups came from populations with different levels of the variable of interest. From this, it follows that the hypotheses in the Mann-Whitney U-test were:  
* **H0. **_The null hypothesis states that the two populations are equal_. * **H1. **_The alternative hypothesis states that the two populations are not equal_. A summary of the proposed features during episodes of AF and non-AF is given in Table 3, as well as the results of the Mann-Whitney U-tests. Only for \(\text{HFD}_{aa}\), REN, and HFD were there no reasons to reject Hypothesis H0. For the remaining features, hypothesis H0 was rejected, demonstrating the existence of a significant difference in the medians of the two distributions. This work used the \(K\)-fold cross-validation method with \(K=5\) to carry out the wrapper method of feature selection, which was performed independently of the LS-SVM model performance test procedure. In this case, each of the five prepared sets of feature vectors served as data for the feature selection method. Then, the feature selection method used only 80% of all feature vectors each time. A genetic algorithm was used to identify the most suitable set of features and executed 10 times for each of the five sets. Overall, this gave 50 different sets of selected features. The objective function of the genetic algorithm was to maximize the prediction accuracy rate, calculated as  
\[Acc=\frac{\mathbf{y}_{\text{test}}{=}\mathbf{y}_{\text{predict}}}{\mathbf{y}_ {\text{test}}}\cdot 100[\%], \tag{24}\]  
where \(\mathbf{y}_{\text{predict}}\) denotes the predicted value of the classifier for the selected set of features, and \(\mathbf{y}_{\text{test}}\) is the known test value. For the feature selection process, we were provided with a dataset that contained 80% of all features. This resulted in a dataset of 30,085 vectors for a given number of total cases \(N=\) 37,606. Of these, 75% (22,564 feature vectors) were used in the operation of the genetic algorithm. The remaining 25% (7521 vectors) were used to determine the accuracy rate (Acc) of the prediction. The manipulations performed prevented the chosen features from being over-matched to the examined database. The feature selection results are shown in Figure 6. It is evident that features \(E_{\text{w2}}\), SpEn, InA\({}_{2}\), Ren, and \(\zeta_{1}\) were the most commonly seen in the set of features that guaranteed the highest value of the classification accuracy factor. On the other hand, it is possible to identify features that appeared less often in the feature sets allowing for high \(Acc\) accuracy values to be achieved. These features were \(\text{HFD}_{aa}\) and HFD, as well as \(\phi_{i}^{2}\) for \(i=1,6,7,8,9\). The selected features were very similar to those selected when the reference methods were used, i.e., ReliefF [68; 69] and MRMR [68; 70]. A comparison of the chosen set of features is displayed in Table 4.