## 4 Discussion  
### Study of Selected Features  
The feature vector comprised 21 elements, which were defined and described in the preceding sections. These elements could be classified into two groups. The first group consisted of a single non-linear HFD indicator calculated using the heart rate [8]. This indicator changed marginally, and the average values for non-AF and AF episodes were comparable, as indicated in Table 3. However, as reported in ref. [8], the Higuchi fractal dimension calculated over RR intervals had remarkably similar values. It is essential to note that the HFD indicator based on RR intervals could not accurately distinguish non-AF and AF episodes. In ref. [8], it was discovered that the non-linear fractal dimension could be used to assess atrial activity, specifically HFD\({}_{aa}\). It is important to note that while both HFD (based on RR intervals) and HFD\({}_{aa}\) were not included in the reduced set of features (as seen in Figure 6), HFD\({}_{aa}\) was used more frequently than HFD in almost all optimal sets of features. This was confirmed by the Mann-Whitney U-test (as seen in Table 3). The results showed that there were no significant differences between the AF and non-AF classes for the parameters HFD\({}_{aa}\) and REN, indicating that the null hypothesis could not be rejected. A similar situation was observed for the HFD feature (\(p\)-value greater than 0). When analyzing the accuracy of the feature sets, the REN and SpEn features (which measured the entropy in the atrial activity part of the ECG signal) were found to be significant in the reduced set of features, as shown in Figure 6.  
The second group of features was calculated on the basis of the atrial activity segment. Table 3 shows a significant difference in the values \(\beta_{2}\) and \(\beta_{3}\) between non-AF and AF \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**File**} & \multicolumn{8}{c}{**Signal**} \\ \cline{2-13}  & **4015** & **4043** & **4048** & **4126** & **4746** & **4908** & **4936** & **5091** & **5121** & **5261** & **6426** & **6453** \\ \hline PPV \% & 76.19 & 98.16 & 100.00 & 94.69 & 100.00 & 100.00 & 99.55 & 57.14 & 98.77 & 100.00 & 99.72 & 100.00 \\ NPV \% & 99.93 & 99.43 & 99.92 & 99.85 & 100.00 & 99.95 & 99.35 & 100.00 & 99.05 & 100.00 & 100.00 & 99.91 \\ Sen \% & 94.12 & 98.16 & 96.30 & 98.17 & 100.00 & 99.49 & 99.77 & 100.00 & 99.56 & 100.00 & 100.00 & 93.75 \\ Spec \% & 99.65 & 99.43 & 100.00 & 99.55 & 100.00 & 100.00 & 98.71 & 99.75 & 97.37 & 100.00 & 92.65 & 100.00 \\ Acc \% & 99.59 & 99.13 & 99.92 & 99.44 & 100.00 & 99.95 & 99.50 & 99.76 & 98.86 & 100.00 & 99.73 & 99.91 \\ F1 \% & 84.21 & 98.16 & 98.11 & 96.40 & 100.00 & 99.75 & 99.66 & 72.73 & 99.16 & 100.00 & 99.86 & 96.77 \\ \hline \multicolumn{13}{c}{**Signal**} \\ \cline{2-13}  & **6995** & **7162 \({}^{*}\)** & **7859 \({}^{*}\)** & **7879** & **7910** & **8215** & **8219** & **8378** & **8405** & **8434** & **8455** \\ \hline PPV \% & 100.00 & 100.00 & 99.93 & 100.00 & 99.91 & 98.09 & 99.22 & 99.93 & 98.70 & 100.00 & \\ NPV \% & 99.35 & n/a & 0.00 & 100.00 & 99.80 & 99.71 & 99.07 & 99.91 & 100.00 & 99.92 & 99.80 \\ Sen \% & 99.34 & 100.00 & 99.95 & 100.00 & 99.11 & 99.91 & 97.05 & 99.74 & 100.00 & 98.70 & 99.93 \\ Spec \% & 100.00 & n/a & n/a & 99.82 & 100.00 & 99.71 & 99.40 & 99.74 & 99.78 & 99.92 & 100.00 \\ Acc \% & 99.67 & 100.00 & 99.95 & 99.95 & 99.84 & 99.86 & 98.84 & 99.74 & 99.95 & 99.85 & 99.95 \\ F1 \% & 99.67 & 100.00 & 99.98 & 99.96 & 99.55 & 99.91 & 97.57 & 99.48 & 99.97 & 98.70 & 99.97 \\ \hline \multicolumn{13}{c}{**[\({}^{*}\)] ECG was 100\% AF.**} \\ \end{tabular}
\end{table} 
Table 6: Detailed LS-SVM classifier results using the selected set of features (rounded to 2 decimal places; n/a if unavailable).