## 2 Results
### WPD-Based Analysis of the HRV Signals  
The WPD method is a generalization of the DWT technique, where both the approximation and the detail coefficients participate in the decomposition process [19]. The HRV signals were subjected to WPD-based decomposition up to level 8 using Daubechies (db02, db04, db06, and db08) mother wavelets [10]. The typical WPD coefficients generated from level 2 decomposition of the HRV signals (using db02 mother wavelet) that belonged to Category-C and Category-B have been shown in Figure 4.  
The extraction and naming of 11 entropy-based parameters were carried out from each of the WPD coefficients, which was similar to that of the DWT-based processing of HRV signals. The Shapiro-Wilk test revealed that majority of the parameters exhibited a non-normal behavior. Hence, their statistical importance was analyzed using the Mann-Whitney U test. The parameters that suggested normal behavior were analyzed for their statistical importance using the \(t\)-test. The results of the statistical test suggested several parameters varied significantly among Category-C and Category-B (\(p\)-value \(\leq\) 0.05). The MD \(\pm\) SD, and the 25 th and 75 th percentile values of the statistically important parameters extracted from db02 mother wavelet-based level 2 decomposition using WPD, have been provided in Table 5 as a typical representation. The judgment of the relevance of the parameters using ranking and dimensionality reduction methods as well as the development of the ML models was carried out similarly to that of DWT-based processing of the HRV signals. The accuracies and AUCs of the most efficient ML models developed from each decomposition level in WPD-based processing of the HRV signals have been provided in Table 6. The NB model generated during level 8 decomposition (db02 mother wavelet) exhibited the highest accuracy of 75.00 \(\pm\) 13.94% among all the ML models that were developed using WPD-based processing of the HRV signals. This model used the top 10 important parameters ranked by the \"SVM\" method as the input parameters. Figure 4: Typical WPD coefficients for db02 mother wavelet-based level 2 decomposition of HRV signals: (**a**) Category-C, and (**b**) Category-B. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method Wavelet**} & \multirow{2}{*}{**Model Details**} & \multicolumn{3}{c}{**Decomposition Level**} \\ \cline{2-9}  & & **Level 2** & **Level 3** & **Level 4** & **Level 5** & **Level 6** & **Level 7** & **Level 8** \\ \hline \multirow{2}{*}{dk02} & Parameter selection method & CSS & Kernel PCA & GI & CSS & IG & SVM & SVM \\  & ML Model & RF & SVM & GEI & NB & FIM & SVM & NB \\  & Accuracy & 64.80 \(\pm\) 798\% & 60.00 \(\pm\) 9.37\% & 73.00 \(\pm\) 5.87\% & 67.00 \(\pm\) 10.85\% & 71.80 \(\pm\) 10.01\% & 74.00 \(\pm\) 9.66\% & 75.00 \(\pm\) 13.84\% \\ \hline \multirow{2}{*}{dk04} & Parameter selection method & CM CM2 & KS & IG & CI & IG & SVM \\  & ML Model & RF & RF & GGT & GST & GST & GST & SVM \\  & Accuracy & 67.00 \(\pm\) 5.87\% & 70.00 \(\pm\) 9.43\% & 67.00 \(\pm\) 12.52\% & 70.00 \(\pm\) 9.43\% & 68.50 \(\pm\) 9.72\% & 69.50 \(\pm\) 9.85\% & 72.00 \(\pm\) 7.15\% \\ \hline \multirow{2}{*}{dk06} & Parameter selection method & CM CM2 & CSS & KR & IG & Correlation & SVM & SOM \\  & ML Model & GST & GGT & GST & GST & GST & GST & NS & RF \\  & Accuracy & 66.00 \(\pm\) 11.01\% & 68.00 \(\pm\) 13.85\% & 64.00 \(\pm\) 11.01\% & 70.50 \(\pm\) 9.89\% & 46.00 \(\pm\) 8.43\% & 67.00 \(\pm\) 12.95\% & 68.00 \(\pm\) 12.52\% \\ \hline \multirow{2}{*}{dk08} & Parameter selection method & \multicolumn{3}{c}{SVM} & \multicolumn{3}{c}{SVM} & \multicolumn{3}{c}{GI} & \multicolumn{3}{c}{CG} & \multicolumn{3}{c}{GI} \\  & ML Model & GGT & GGT & GGT & GGT & IG & GS & GST \\  & Accuracy & 66.00 \(\pm\) 8.43\% & 68.00 \(\pm\) 11.35\% & 68.50 \(\pm\) 12.92\% & 64.00 \(\pm\) 11.25\% & 67.50 \(\pm\) 8.82\% & 70.50 \(\pm\) 8.82\% & 73.00 \(\pm\) 8.85\% \\ \hline \hline \end{tabular}
\end{table}