## 4 Feature Extraction  
During the extraction section, information from PPG derivatives is taken into account. As [PERSON], [PERSON] et al. mentioned in their study, information from the first and second derivatives of PPG signals is related to atherosclerosis and vascular elasticity, which are the factors that influence BP [34, 35], and consequently shows a better correlation with BP inside the blood vessel. Based on this, twenty-one key features are extracted from the PPG signal, its derivative shape, and the time shift between the ECG and PPG wave. All this computation is parameter-based rather than a whole-based extraction of characteristics in the ECG-PPG window linked to BP.  
Previous studies have also demonstrated that BP can indeed be deduced from the PTT [36, 37], as it is the time interval needed for the arterial pulse to shift from one peripheral to another, which is an indicator of variation in BP. In our model, pulse arrival time (PAT) is then used to measure the targeted pulse transit time since they all have the same amount in representation but differ from the fact that PAT considers a short period during the electrical activation of the heart.  
As mentioned previously, PAT is preferred by researchers due to its ease of measurement and its representation of the time interval between the R peak in the ECG and the pulse at the distal end. The time event of ECG and PPG peak (PAT), maximum slope point (PAT\({}_{d}\)), and diastolic foot (PAT\({}_{f}\)) are emphasized in our study, as well as the pulse intensity ratio (PIR), which has also been shown to be inversely proportional to the DBP. And the features are listed as follows:  
_PPG_'s 1 st derivative (maximum intensity (dppgH, f1); period between max and min values (dppgW, f2); maximum slope point (PAT\({}_{d}\), f3)), PPG's 2 nd derivative (maximum intensity (ddppgPH, f4); (ddppgFH, f5)), peaks (systolic time (ST, f6); diastolic time (DT, f7);  
Figure 2: ECG (1a–1d); PPG (2a–2d); and ABP (3a–3d) window that work for the feature extraction pipeline.  
pulse intensity ratio (PIR, f\({}_{8}\)); heart rate (HR, f\({}_{9}\)); systolic peak (PAT\({}_{\rm p}\), f\({}_{10}\)); diastolic peak (PAT\({}_{\rm p}\), f\({}_{11}\))), and PPG (inverse of the elapsed time bounded by the PPG maximum and the stationary point: artery stiffness index (ASI, f\({}_{12}\)), areas under the curve starting from the diastolic foot to the max slop (AUC, f\({}_{13}\)), from the max slop to systolic peak (AUC, f\({}_{14}\)), from systolic peak to the stationary point (AUC, f\({}_{15}\)), and from the stationary point to the next PPG diastolic foot (AUC, f\({}_{16}\)), and the area under curve ratio: inflection point area (IPA, f\({}_{17}\))). IPA evaluates the total peripheral resistance of the blood vessel to blood flow. there may be an inflection point in the trajectory of BP preceding hypertension onset, and the BP will rise relatively faster after the inflection point [38].  
\[IPA=\frac{AUC4}{AUC1+AUC2+AUC3} \tag{2}\]  
Our 7057 windows from the processing pipeline were fed into the _FeatureExtraction.m_ function as an input. In this section, four extraction parts took place and windows can be interpreted as samples for the estimated model. The first part allows the extraction of ST, DT, PIR, and PPG where the PPG signal is processed with no peaks and feet detected and 765 rejected. The second part consisted of extracting the first and second derivatives of PPG, AI, ASI, and HR, but the first dPPG peaks and feet failed to be detected, with four rejected. The areas under curves (AUC1, AUC2, AUC3, and AUC4) were extracted in the third part as well as IPA, PAT\({}_{\rm d}\), PAT\({}_{\rm f}\), and PAT\({}_{\rm p}\), but we rejected 23 here. The last and fourth parts allowed us to extract SBP and DBP with an output window of 6265.