## 3 Background
### Binary Harris Hawks Optimizer (BHHO)
Figure 8 compares raw and filtered PPG signals, demonstrating significant noise reduction. This filtering step was critical for accurately detecting systolic peaks, as shown in Figure 9, which illustrates the detected peaks in a filtered PPG signal. After preprocessing, the dataset size was refined to 190 trials by removing samples with excessive noise or unclear peaks. These clean signals formed the foundation for robust feature extraction. ### Results of Feature Extraction

Following signal preprocessing, the next phase involved extracting meaningful features from the PPG signals. A total of 69 features were extracted, including 19 time-domain features, 5 frequency-domain features, and 43 non-linear HRV features, as well as 2 features directly derived from the waveform, including PPG Rate Mean (HR) and PPG Rate Standard Deviation, as described in Section 4.1.3. Table 2 presents a summary of the extracted features, and Supplementary Table S1 provides detailed lists of the HRV features computed [59]. The final feature vector can be derived as Equation (21) for each frame \(\mathbf{f}\) of the PPG signals. These features were then reduced by metaheuristic-based feature selection algorithms in a later stage. \[S= \begin{bmatrix}\text{PPG\_Rate\_Mean}&\text{PPG\_Rate\_SD}&\text{RMSSD}& \text{MeanNN}&\text{SDNN}&\text{SDSD}&\text{CVNN}\\ \text{MadNN}&\text{MCVNN}&\text{IQRNN}&\text{pNNN50}&\text{pNN20}&\text{HTI}& \text{TINN}\\ \text{MinNN}&\text{MaxNN}&\text{SDRMSSD}&HF&\text{\emph{VHF}}&\text{\emph{TP}}& \text{\emph{HFn}}\\ \text{SD2}&\text{SD1}/\text{SD2}&S&\text{CS1}&\text{\emph{CVI}}&\text{CSL\_Modified}& \text{\emph{PIP}}\\ \text{PAS}&\text{GI}&\text{SI}&\text{\emph{AI}}&\text{\emph{PI}}&\text{Cl1}& \text{Cla}\\ \text{C2d}&\text{C2a}&\text{SD2d}&\text{SD2a}&\text{Cd}&\text{\emph{Ca}}& \text{\emph{SDNNd}}\\ \text{APEn}&\text{ShanEn}&\text{FuzzEn}&\text{CD}&\text{\emph{HFD}}&\text{ \emph{KFD}}&\text{\emph{IZC}}\\ (\text{Ma1})\_Delta&(\text{Ma1})\_Max&(\text{Ma1})\_Asym.&(\text{Ma1})\_Fluct.&(\text{Ma1})\_Incr \text{Prc20 NN}&\text{Prc80 NN}&\text{LnHF}&\text{\emph{SD1}}&\text{\emph{SD1 }}&\text{\emph{PSS}}&\text{\emph{SD1d}}\\ \text{SD1a}&\text{SDNNa}&\text{DFA\_alpha1}&\text{MEDPA\_alpha1}(\text{Ma1})\_ Peak&(\text{Ma1})\_Mean&\end{bmatrix} . \tag{21}\] \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Feature Type**} & \multicolumn{2}{c}{**Number of Features**} & \multicolumn{2}{c}{**Example Features**} \\ \cline{2-5}  & & **Features** & & & \\ \hline Time-Domain & 19 & & RMSSD, PNN50 \\ Frequency-Domain & 5 & & LF/HF Ratio, Power Spectral Density \\ Non-linear Methods & 43 & & DFA Alpha, Poincare SD1/SD2 \\ PPG Derived Features & 2 & PPG Rate Mean, PPG Rate Standard Deviation \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of extracted features. Figure 8: Comparison of raw (**left**) and filtered (**right**) PPG signals. Figure 9: Detection of systolic peaks in a filtered PPG signal. ### Metaheuristic Algorithms and Machine Learning Models (Parameter Settings)

Each metaheuristic algorithm operated with uniform configurations of 50 iterations and a population size of 50 to ensure consistency across all experiments. The parameter configurations for each algorithm were derived from prior literature to balance exploration and exploitation effectively [26; 27; 30; 48]. Table 3 summarizes these configurations. IDA utilized a sinusoidal function for dynamic exploration-exploitation balancing, as recommended in the IDA paper [26], while BHHO applied a beta value of 0.5 for Levy flight-based exploration, as suggested by [PERSON] hyperparameter settings [48]. bGWO employed a sigmoidal function with stochastic thresholding for binary position updates [27], and GA was configured with a selective pressure of two, an elitism of two, and a mutation rate of 0.05 to optimize performance [48]. For the regression models, hyperparameter tuning was performed iteratively to minimize MAE, ensuring optimal model performance.