## 3 Results
### Discrimination between Older NSR and CHF Participants  
Table 3 shows the discrimination accuracies between NSR and CHF in older individuals under various conditions. Even though average MSE values from the NN interval time series for older NSR and CHF participants did not appear different from one another (Figure 2), accuracy reached 90.1% \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Data Sample Sizes** & **Scales** & **Acc** & **Sen** & **Spe** & **PPV** & **NPV** \\ \hline
40,000 & [4, 5, 6, 9] & 97.7 & 94.4 & 100.0 & 100.0 & 96.3 \\ \hline
20,000 & [1, 6, 9, 13] & 90.9 & 83.3 & 96.2 & 93.8 & 89.3 \\  & [1, 6, 9, 18] & 90.9 & 83.3 & 96.2 & 93.8 & 89.3 \\  & [1, 6, 9, 19] & 90.9 & 83.3 & 96.2 & 93.8 & 89.3 \\ \hline
10,000 &
\begin{tabular}{c} [3, 4, 11, \\ 16] \\ \end{tabular} & 93.2 & 88.9 & 96.2 & 94.1 & 92.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: List of the combinations of the optimal four MSE (\(r\) = 0.20) scales for discrimination between young NSR and CHF participants using LDA, as well as their corresponding accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. Note: Acc, accuracy; Sen, sensitivity; Spe, specificity; PPV, positive predictive value; NPV, negative predictive value. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \multicolumn{10}{c}{**LDA**} \\ \hline  & \multicolumn{3}{c}{**2D**} & \multicolumn{3}{c}{**3D**} & \multicolumn{3}{c}{**4D**} & \multicolumn{3}{c}{**5D**} \\ \(r\) & **0.10** & **0.15** & **0.20** & **0.10** & **0.15** & **0.20** & **0.10** & **0.15** & **0.20** & **0.10** & **0.15** & **0.20** \\ \hline
40,000 & 95.5 & 90.9 & 93.2 & 95.5 & 95.5 & 95.7 & 97.5 & 95.7 1 & 97.7 & 97.7 & 97.7 \\
20,000 & 86.4 & 86.4 & 86.4 & 86.4 & 86.4 & 88.6 & 88.6 & 88.6 & 90.9 1 & 88.6 & 88.6 & 90.9 \\
10,000 & 86.4 & 86.4 & 89.6 & 88.6 & 90.9 & 90.9 & 88.6 & 93.2 & 93.2 1 & 90.9 & 93.2 & 93.2 \\ Mean \(\pm\) SD & \multicolumn{3}{c}{83.6 \(\pm\) 4.7} & \multicolumn{3}{c}{90.9 \(\pm\) 3.6} & \multicolumn{3}{c}{92.7 \(\pm\) 3.5} & \multicolumn{3}{c}{93.2 \(\pm\) 3.6} \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \multicolumn{10}{c}{**SVM**} \\ \hline  & \multicolumn{3}{c}{**2D**} & \multicolumn{3}{c}{**3D**} & \multicolumn{3}{c}{**4D**} & \multicolumn{3}{c}{**5D**} \\ \(r\) & **0.10** & **0.15** & **0.20** & **0.10** & **0.15** & **0.20** & **0.10** & **0.15** & **0.20** & **0.10** & **0.15** & **0.20** \\ \hline
40,000 & 93.2 & 90.9 & 90.9 & 93.2 & 90.9 & 90.9 & 90.9 & 90.9 & 90.9 & 90.9 & 90.9 & 90.9 & 90.9 \\
20,000 & 88.6 & 88.6 & 88.6 & 88.6 & 88.6 & 88.6 & 88.6 & 88.6 & 88.6 & 88.6 & 88.6 \\
10,000 & 81.8 & 79.5 & 84.1 & 86.4 & 88.6 & 88.6 & 88.6 & 88.6 & 88.6 & 88.6 & 88.6 \\ Mean \(\pm\) SD & \multicolumn{3}{c}{87.4 \(\pm\) 4.3} & \multicolumn{3}{c}{89.1 \(\pm\) 2.1} & \multicolumn{3}{c}{88.6 \(\pm\) 1.9} & \multicolumn{3}{c}{88.6 \(\pm\) 1.8} \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{10}{c}{**CNN**} \\ \hline  & \multicolumn{3}{c}{**2D**} & \multicolumn{3}{c}{**3D**} & \multicolumn{3}{c}{**4D**} & \multicolumn{3}{c}{**5D**} \\ \(r\) & **0.10** & **0.15** & **0.20** & **0.10** & **0.15** & **0.20** & **0.10** & **0.15** & **0.20** & **0.10** & **0.15** & **0.20** \\ \hline
40,000 & 88.6 & 88.6 & 90.9 & 90.9 & 88.6 & 90.9 & 90.9 & 88.6 & 90.9 & 88.6 & 88.6 & 90.9 \\
20,000 & 86.4 & 86.4 & 86.4 & 84.1 & 88.6 & 88.6 & 86.4 & 86.4 & 88.6 & 86.4 & 86.4 \\
10,000 & 81.8 & 84.1 & 84.1 & 81.8 & 84.1 & 86.4 & 84.1 & 86.4 & 86.4 & 88.6 & 86.4 \\ Mean \(\pm\) SD & \multicolumn{3}{c}{86.4 \(\pm\) 2.6} & \multicolumn{3}{c}{86.9 \(\pm\) 3.0} & \multicolumn{3}{c}{87.6 \(\pm\) 2.2} & \multicolumn{3}{c}{87.9 \(\pm\) 1.5} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Discrimination accuracy between young NSR and CHF participants using linear discriminant analysis (LDA), support vector machine (SVM), and k-nearest neighbor (KNN).