## 3 Background
### Binary Harris Hawks Optimizer (BHHO)
Figure 6: Heart Rate (HR) and its mean value extraction from PPG signal: The time interval (period) between consecutive systolic peaks (**left**), The time interval of bpm to measure the Heart Rate (HR) in beats per minute (bpm) (**right**). characterize dynamic patterns. Table 1 provides an overview of HRV metrics across these domains. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Analysis** & **Sub-Domain** & **Acronym** & **Description** \\ \hline \multirow{6}{*}{Time-Domain} & & SDNN & Standard deviation of NN intervals \\ \cline{3-4}  & Deviation-based & SDANN & Standard deviation of average NN intervals for each \\  & approach & & 5 min segment \\ \cline{3-4}  & & SDNN Index & Mean of the standard deviations of NN intervals in \\  & & & 5 min segments \\ \cline{3-4}  & & SDSD & Standard deviation of successive NN interval differences \\ \cline{3-4}  & & RMSSD & Root mean square of successive NN interval differences \\ \cline{3-4}  & Difference-based & pNN20 & Proportion of successive NN interval differences larger than \\  & approach & & 20 ms \\ \cline{3-4}  & & pNN50 & Proportion of successive NN interval differences larger than \\  & & & 50 ms \\ \cline{3-4}  & Geometric approach & HTI & Integral of the density of the NN interval histogram divided by its height \\ \cline{3-4}  & & TTIN & Baseline width of the RR interval histogram \\ \hline \multirow{6}{*}{Frequency-domain} & & ULF & Power spectrum in the frequency range of \(\leq\)0.003 Hz \\ \cline{3-4}  & Absolute Power & VLF & Power spectrum in the frequency range of 0.003–0.04 Hz \\ \cline{3-4}  & & LF & Power spectrum in the frequency range of 0.04–0.15 Hz \\ \cline{3-4}  & & HF & Power spectrum in the frequency range of 0.15–0.4 Hz \\ \cline{3-4}  & & LnHF & Natural logarithm of HF \\ \cline{3-4}  & Normalized/Relative & HFn & Normalized HF \\ \cline{3-4}  & Power & LFn & Normalized LF \\ \cline{3-4}  & & LF/HF & Ratio of LF to HF power \\ \hline \multirow{6}{*}{Time-frequency domain} & & STFT & Power spectrum estimation using short-term Fourier transform \\ \cline{3-4}  & Linear approach & WT & Power spectrum estimation using Wavelet transform \\ \cline{3-4}  & & WVD & Power spectrum estimation using Wigner—Ville distribution \\ \cline{3-4}  & Quadratic approach & SWVD & Power spectrum estimation using Smoothed Wigner–Ville distribution \\ \cline{3-4}  & & SD1 & Poincaré plot standard deviation perpendicular the line of \\  & Poincaré Plot & & identity \\ \cline{3-4}  & & SD2 & Poincaré plot standard deviation along the line of identity \\ \cline{3-4}  & & SD1/SD2 & Ratio of SD1 to SD2 \\ \cline{3-4}  & & ApEn & Estimation of complexity using approximate entropy \\ \cline{3-4}  & entropy Entropy & & SampEn & Estimation of complexity using sample entropy \\ \cline{3-4}  & & MSE & Estimation of complexity using multiscale entropy \\ \cline{3-4}  & & DFA & Estimation of signal fluctuations using detrended \\ \cline{3-4}  & Fractal Dimensions & & fluctuation analysis \\ \cline{3-4}  & & CD & Estimation of minimum number of variables to define a \\ \cline{3-4}  & & & dynamic model \\ \hline \hline \end{tabular}
\end{table} 
Table 1: An overview of HRV metrics categorized by their analysis domains [44]. The NeuroKit2 library [40] was employed to automate and streamline feature extraction. After computation, the features were consolidated into a database, with BGL as the target variable. Missing values were addressed through mean imputation to ensure dataset consistency [45], and normalization was applied using min-max scaling, transforming values to the range [0, 1] as follows [46; 47]:

\[x^{\prime}=\frac{x-x_{min}}{x_{max}\ -x_{min}} \tag{17}\]

where x\({}^{\prime}\) is the normalized value, x is the original value, and \(x_{min}\) and \(x_{max}\) are the minimum and maximum values, respectively. This preprocessing step made the features scale compatible and reduced the variability because of different scales of the features. Such steps of preparation made the dataset ready for robust analysis and thus allowed predicting the BGL effectively.