## 2 Results  
### EMD-Based Analysis of the HRV Signals  
EMD has been reported to be an efficient method for the processing of nonlinear and non-stationary data [8]. This may be attributed to its adaptive nature [11]. The HRV signals were subjected to EMD-based decomposition. The decomposition resulted in the generation of at least seven IMFs for each of the HRV signals. Hence, seven IMFs were considered for further analysis [12]. The typical IMFs for HRV signals belonging to Category-C and Category-B have been shown in Figure 1.  
Figure 1: Typical IMFs of 5 min HRV signals: **(a)** Category-C and **(b)** Category-B. The extraction of 11 entropy-based parameters (described in Section 4.3) was carried out from each of the IMFs. The extracted parameters were named XY, where X represents the IMF name and Y indicates the base name of the parameter. Out of the 77 parameters extracted from 7 IMFs, 49 parameters were found to have non-normal distribution, and the remaining 28 parameters exhibited normal distribution as per the Shapiro-Wilk Test. The Mann-Whitney U test (with a critical _p_-value of 0.05) was used to analyze the statistical importance of the IMF parameters having non-Gaussian distribution. It revealed that the IMF7 SpE parameter was significantly different (_p_-value \(\leq\) 0.05) among Category-C and Category-B. The statistical importance testing using the _t_-test for the parameters having Gaussian distribution suggested four significantly different parameters among Category-C and Category-B. The statistically different parameters include IMF5 SVDE, IMF5 SpE, IMF5 FI, and IMF5 HjC. The median (MD) \(\pm\) standard deviation (SD), and the25 th and 75 th percentile values of these parameters, have been tabulated in Table 1.  
Among all the extracted entropy-based parameters, the parameters that ranked within the top 10 by the weight-based parameter ranking methods and the parameters suggested by the dimensionality reduction methods were used as input for developing nine ML models as discussed in Section 4.5.2. As a result, 135 ML (9 ML models \(\times\) 15 weight-based selection methods) models were developed from 15 weight-based parameter ranking methods, and 45 ML (9 ML models \(\times\) 5 dimensionality reduction methods) models were obtained from 5 dimensionality reduction methods. The most accurate ML models (out of the nine ML models) along with their performance measures generated from each parameter selection method are shown in Table 2[17]. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multirow{2}{*}{**Statistical Test Name**} & \multicolumn{3}{c}{**Category-C**} & \multicolumn{3}{c}{**Category-B**} & \multirow{2}{*}{\(p\)**-Value**} \\ \cline{3-3} \cline{5-8}  & & **MD \(\pm\) SD** & **25 th** & **75 th** & **MD \(\pm\) SD** & **25 th** & **75 th** \\ \hline IMF5 SVDE & _t_-test & 0.329 \(\pm\) 0.064 & 0.288 & 0.377 & 0.355 \(\pm\) 0.054 & 0.318 & 0.385 & 0.010 \\ IMF5 SpE & _t_-test & 4.088 \(\pm\) 0.450 & 3.756 & 4.380 & 4.245 \(\pm\) 0.523 & 3.825 & 4.555 & 0.049 \\ IMF5 FI & _t_-test & 0.884 \(\pm\) 0.029 & 0.862 & 0.902 & 0.872 \(\pm\) 0.025 & 0.858 & 0.889 & 0.011 \\ IMF5 HjC & _t_-test & 0.076 \(\pm\) 0.021 & 0.063 & 0.092 & 0.085 \(\pm\) 0.018 & 0.071 & 0.096 & 0.012 \\ IMF5 SpE & Mann-Whitney U & 2.420 \(\pm\) 0.774 & 2.065 & 2.847 & 2.680 \(\pm\) 0.604 & 2.359 & 3.163 & 0.004 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Characteristics of statistically important IMF-derived parameters. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{\ \begin{tabular}{c} **Input** \\ **Selection** \\ **Methods** \\ \end{tabular}  } & **Feature** & **Classifier** & **Accuracy (\%)** & **AUC** & **Precision (\%)** & **F-Measure (\%)** & **Sensitivity (\%)** & **Specificity (\%)** \\ \hline \multirow{8}{*}{ \begin{tabular}{c} Weight- \\ based (Top \\ 10 important parameters) \\ \end{tabular}