## 5 Deep Learning CNN--Methodology  
CNN was performed to quantify features that cause considerable variation in the dataset and eliminate those that are responsible for less variation. This technique is relevant in our study since the number of predictors is reduced and the computing capacity of the model's training process is improved. Apart from performing CNN, another important step that increased the computation capacity of the training process even more was computing cross-validation tests such as leave-one-out. Since our training procedure was repeated over time, a stepwise multivariate linear regression was also performed to enable us to statistically select features that would work for our model. Having such a large predictor vector may increase computation time, which is why we considered reducing the number of predictors. Furthermore, in this section, an iterative construction of a regression model was performed and involved choosing predictors or features that are utilized in the final model. The regression model consists of adding or removing potential explanatory features in succession and testing for statistical significance after each iteration. The \(p\)-value (with predefined threshold = 0.05) of an F-statistic test was computed and compared to measure the likeliness of having a null coefficient. The latter was computed based on the least square technique. The linear regression model could estimate the systolic BP and diastolic BP values. \[SBP=\alpha_{0}+\alpha_{1}f_{1}+\alpha_{2}f_{2}+\cdots+\alpha_{n}f_{n} \tag{3}\]  
\[DBP=\beta_{0}+\beta_{1}f_{1}+\beta_{2}f_{2}+\cdots+\beta_{n}f_{n} \tag{4}\]  
During our experiment, the deep learning method was carried out for the measurements. We hope that deep learning will reduce error rates as a result of these measurements. DeepPhys [39] provides a visualization of physiological information in videos using convolutional attention networks. Eighteen participants from DeepPhys were recorded with a VF0800 camera, equipped with an Intel RealSense by Intel corporation in Santa Clara, CA, USA. The average age of the participants is 37, both men and women were recruited. Videos with 1920 \(\times\) 1080-pixel resolution were registered with a 24 fps frame rate in 24-bit RGB color. A FlexComp Infiniti was used to evaluate gold-standard physiological signalsand the blood volume pulse (BVP). The details of the hyperparameters used are shown in Table 1.  
The algorithm processes RGB or infrared videos and can accurately obtain heart rate. PhysNet's main purpose is to use a spatiotemporal network for rPPG signals from videos and extract spatial and temporal hidden features simultaneously from raw face sequences, as in [40]. Then, it compares rPPG signals with Ground Truth ECG values. The method makes peak detection to find the interbeat interval for average HR and HR variability. It creates linear models capable of predicting SBP and DBP values (Figure 3). Root mean square error (RMSE) is the STD of the prediction errors called residuals. Residuals can be measured by how far data points are from the regression line. The RMSE value is a measure of how far these residues have spread. The RMSE value can be calculated as follows:  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{N}(Predicted_{i}-\text{Acual}_{i})^{2}}{N}} \tag{5}\] \begin{table}
\begin{tabular}{l l} \hline \hline
**Hyper-Parameter** & **Value** \\ \hline Optimizer & Adam \\ \hline Weight & ImageNet \\ \hline Metrics & Accuracy \\ \hline Epoch & 30 \\ \hline Batch size & 32 \\ \hline Drop out & 20\% \\ \hline Number of hidden units in layer & 128 \\ \hline \end{tabular}
\end{table} 
Table 1: Hyper-parameters used in this work. Figure 3: Flowchart of the process algorithm. Acquisition of video data: this process initiates with the acquisition of video data, wherein the facial region of a subject is aimed to be captured continuously over a certain period of time. The quality and clarity of the video are pivotal, ensuring the algorithms can effectively discern features necessary for subsequent analyses.