## 3 Results
### Feature Selection Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **This Work** & **ReliefF [68; 69]** & **MRMR [68; 70]** \\ \hline
1 & \(E_{\textbf{w2}}\) & \(\beta_{2}\) & \(\beta_{2}\) \\
2 & SpEn & REN & \(\beta_{3}\) \\
3 & \(\beta_{2}\) & \(E_{\textbf{w2}}\) & \(\hat{\phi}_{9}^{2}\) \\
4 & REN & \(\beta_{3}\) & \(\zeta_{1}\) \\
5 & \(\zeta_{1}\) & \(\zeta_{1}\) & SaEn \\
6 & \(\beta_{3}\) & \(E_{\textbf{w1}}\) & \(E_{\textbf{w1}}\) \\
7 & \(\zeta_{2}\) & \(\zeta_{2}\) & \(E_{\textbf{w2}}\) \\
8 & \(\hat{\phi}_{2}^{2}\) & SpEn & TPN \\
9 & \(E_{\textbf{w1}}\) & TPN & \(\zeta_{2}\) \\
10 & TPN & HFD & \(\hat{\phi}_{4}^{2}\) \\
11 & SaEN & SaEN & HFD\({}_{aa}\) \\
12 & \(\hat{\phi}_{8}^{2}\) & \(\hat{\phi}_{5}^{2}\) & \(\hat{\phi}_{1}^{2}\) \\
13 & \(\hat{\phi}_{3}^{2}\) & \(\hat{\phi}_{3}^{2}\) & REN \\
14 & \(\hat{\phi}_{4}^{2}\) & \(\hat{\phi}_{2}^{2}\) & \(\hat{\phi}_{5}^{2}\) \\
15 & \(\text{HFD}_{aa}\) & \(\hat{\phi}_{4}^{2}\) & \(\hat{\phi}_{3}^{2}\) \\
16 & \(\hat{\phi}_{8}^{2}\) & \(\hat{\phi}_{8}^{2}\) & SpEn \\
17 & \(\hat{\phi}_{6}^{2}\) & \(\hat{\phi}_{7}^{2}\) & \(\hat{\phi}_{8}^{2}\) \\
18 & \(\hat{\phi}_{7}^{2}\) & \(\hat{\phi}_{6}^{2}\) & \(\hat{\phi}_{8}^{2}\) \\
19 & \(\hat{\phi}_{7}^{2}\) & \(\hat{\phi}_{8}^{2}\) & \(\hat{\phi}_{8}^{2}\) \\
20 & \(\hat{\phi}_{9}^{2}\) & \(\hat{\phi}_{1}^{2}\) & HFD \\
21 & HFD & \(\hat{\phi}_{9}^{2}\) & \(\hat{\phi}_{5}^{2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Feature importance scores (in descending order) obtained using method proposed in this work and the reference methods ReliefF [68; 69] and MRMR [68; 70] (the features rejected in the proposed method are highlighted in gray). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Non-AF**} & \multicolumn{2}{c}{**AF**} \\ \hline
**Feature** & **Mean** & **Std** & **Mean** & **Std** & \(p\)**-Value** & **h** \\ \hline \(E_{\textbf{w1}}\) & 26.4109 & 9.8015 & 20.5394 & 8.2889 & 0 & 1 \\ \(E_{\textbf{w2}}\) & 23.3416 & 8.6224 & 18.7514 & 9.8648 & 0 & 1 \\ \(\beta_{2}\) & 0.0005 & 0.0005 & \(-0.0001\) & 0.0003 & 0 & 1 \\ \(\beta_{3}\) & 0.0004 & 0.0006 & \(-0.0002\) & 0.0004 & 0 & 1 \\ HFD\({}_{aa}\) & 1.1206 & 0.1257 & 1.1416 & 0.1638 & 0.2572 & 0 \\ TPN & 13.5132 & 4.1173 & 13.1830 & 5.5986 & 0 & 1 \\ SpEn & 0.9288 & 0.0044 & 0.9288 & 0.0046 & 0 & 1 \\ REN & 1.6723 & 1.4262 & 1.8316 & 1.8735 & 0.3374 & 0 \\ SaEN & 0.1471 & 0.0867 & 0.1918 & 0.0984 & 0 & 1 \\ HFD & 1.9701 & 0.3028 & 1.9714 & 0.1364 & 0.0007 & 1 \\ \(\zeta_{1}\) & 5.8912 & 2.6763 & 4.6510 & 1.5945 & 0 & 1 \\ \(\zeta_{2}\) & 3.3973 & 0.8831 & 3.8139 & 1.2100 & 0 & 1 \\ \(\hat{\phi}_{1}^{2}\) & 0.0002 & 0.0004 & 0.0002 & 0.0002 & 0 & 1 \\ \(\hat{\phi}_{2}^{2}\) & 0.0017 & 0.0016 & 0.0017 & 0.0019 & 0 & 1 \\ \(\hat{\phi}_{3}^{2}\) & 0.0108 & 0.0101 & 0.0095 & 0.0101 & 0 & 1 \\ \(\hat{\phi}_{4}^{2}\) & 0.0234 & 0.0291 & 0.0198 & 0.0237 & 0 & 1 \\ \(\hat{\phi}_{5}^{2}\) & 0.0254 & 0.0446 & 0.0286 & 0.0452 & 0 & 1 \\ \(\hat{\phi}_{6}^{2}\) & 0.0273 & 0.0689 & 0.0347 & 0.0686 & 0 & 1 \\ \(\hat{\phi}_{7}^{2}\) & 0.0145 & 0.0324 & 0.0196 & 0.0396 & 0 & 1 \\ \(\hat{\phi}_{8}^{2}\) & 0.0021 & 0.0059 & 0.0017 & 0.0033 & 0 & 1 \\ \(\hat{\phi}_{9}^{2}\) & 0.0003 & 0.0025 & 0.0005 & 0.0009 & 0 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary characteristics of features used in this work for non-AF and AF, and the results of the Mann–Whitney U-test (\(\alpha=0.01\), \(h=1\) indicates the rejection of the null hypothesis \(\text{H}_{0}\)). Two sets of features are visually compared with t-SNE (t-distributed stochastic neighbor embedding) in Figure 7[71]. It can be seen that with a complete set of features, the boundaries between the two classes (non-AF and AF cases) often overlapped in many areas, while reducing the number of features to 14 led to the better separation of the two classes.