## 2 Results
### DWT-Based Analysis of the HRV Signals  
DWT is a popular joint time-frequency analysis method that helps to detect subtle variations in the signals [10]. The DWT-based decomposition of the HRV signals was performed from level 2 up to level 8 using Daubechies (db02, db04, db06, and db08) mother wavelets [10]. Hence, 28 different sets of DWT wavelet coefficients were generated in our study. The representative DWT coefficients generated for level 6 decomposition (using db02 mother wavelet) of HRV signals that belonged to Category-C and Category-B, respectively, have been shown in Figure 3.  
A total of 11 entropy-based parameters were extracted from each wavelet coefficient. The extracted parameters were named as PQ, where P corresponded to the base name of the parameter and Q indicated the index of the wavelet coefficients. The index of the wavelet coefficients started from 0, i.e., the 1 st wavelet coefficient was assigned with the index number 0. For each of the parameters extracted from the 28 different sets of DWT wavelet coefficients, the nature of distribution was found using the Shapiro-Wilk test. The results suggest that the majority of the parameters have non-Gaussian distribution, and the Mann-Whitney U test was performed to test their statistical significance. The \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \begin{tabular}{c} **Input** \\ **Methods** \\ \end{tabular} & \begin{tabular}{c} **Feature** \\ **Selection** \\ **Methods** \\ \end{tabular} & \begin{tabular}{c} **Classifier** \\ **Accuracy (\%)** \\ \end{tabular} & \begin{tabular}{c} **Accuracy (\%)** \\ \end{tabular} & \begin{tabular}{c} **AUC** \\ **Precision (\%)** \\ \end{tabular} & \begin{tabular}{c} **Precision (\%)** \\ \end{tabular} & \begin{tabular}{c} **F-Measure (\%)** \\ \end{tabular} & \begin{tabular}{c} **Sensitivity (\%)** \\ \end{tabular} & \
\begin{tabular}{c} **Specificity (\%)** \\ \end{tabular} \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Dimension \\ Reduction \\ \end{tabular} } & ICA & GBT & \(58.00\pm 11.11\%\) & \(0.609\pm 0.117\) & \(60.30\pm 17.06\%\) & \(58.06\pm 10.35\%\) & \(58.00\pm 12.29\%\) & \(58.00\pm 18.74\%\) \\ \cline{2-8}  & ICA & SVM & \(60.50\pm 9.85\%\) & \(0.613\pm 0.139\) & \(60.99\pm 11.44\%\) & \(62.08\pm 9.50\%\) & \(66.00\pm 17.13\%\) & \(55.00\pm 19.58\%\) \\ \cline{2-8}  & Kernel PCA & GBT & \(64.00\pm 14.68\%\) & \(0.656\pm 0.156\) & \(66.38\pm 17.96\%\) & \(60.12\pm 17.62\%\) & \(56.00\pm 19.55\%\) & \(72.00\pm 14.76\%\) \\ \cline{2-8}  & SVD & RF & \(58.00\pm 9.49\%\) & \(0.551\pm 0.142\) & \(67.58\pm 23.68\%\) & \(38.56\pm 18.28\%\) & \(28.00\pm 15.49\%\) & \(28.00\pm 15.49\%\) \\ \cline{2-8}  & SOM & RF & \(62.00\pm 7.89\%\) & \(0.647\pm 0.102\) & \(61.32\pm 9.76\%\) & \(60.77\pm 13.44\%\) & \(62.00\pm 18.74\%\) & \(62.00\pm 12.29\%\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._  
Figure 2: A typical classification tree generated by the GBT Model. parameters having Gaussian distribution were subjected to the \(t\)-test to examine their statistical importance. The MD \(\pm\) SD, and the 25 th and 75 th percentile values of the statistically important parameters that were extracted from only db02 mother wavelet-based level 2 decomposition, have been provided in Table 3 as a typical representation. The parameters were examined for their relevance in the development of ML models using weight-based ranking and dimensionality reduction methods. In the case of the ranking methods, the parameters which ranked within the top 10 were considered relevant. The relevant parameters were used as inputs for the development of the ML models. The accuracies and AUCs of the most efficient ML models developed from the various levels of decomposition of the HRV signals have been provided in Table 4, [17]. The GBT model generated during level 8 decomposition (using db08 as the mother wavelet) exhibited the highest accuracy of 73.00 \(\pm\) 5.87.