## 2 Methods  
Figure 1 shows the block diagram of the GFSDP algorithm. The first stage was obtained using PPG and ECG signals, as shown in Figure 1a. At this stage, a public dataset was acquired from the University of California Irvine (UCI) ML repository center [28]. Outliers were removed in the preprocessing stage. Valuable features were extracted after preprocessing as they are essential for accurate BP and CI estimations from ECG and PPG signals, as shown in Figure 1c. Subsequently, the hybrid F-test [38] and RNCA [39]  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Explanation** & **Measurement** & **Goal** \\ \hline Combining bootstrap with Gaussian mixture & Oscillometric & BPs, CIs [5] \\ Accuracy and “range of uncertainty” of oscillometric blood pressure monitors & Oscillometric & BPs, CIs [6] \\ BP measurement based on physiological arterial pressure variability & Oscillometric & BPs, CIs [7] \\ Estimated confidence interval from single pressure measurement & Oscillometric & BPs, CIs [9] \\ Uncertainty in blood pressure measurement & Oscillometric & BPs, CIs [10] \\ Ensemble methodology for confidence interval in oscillometric BP measurements & Oscillometric & BPs, CIs [11] \\ Confidence interval estimation for oscillometric BP measurements using bootstrap approaches & Oscillometric & BPs, CIs [8] \\ Uncertainty using the assuculatory method & Auscuulatory & BPs, CIs [84] \\ Improving cuffless continuous BP Estimation with linear regression analysis & PPG, ECG & BPs [13] \\ Long short-term memory networks with transfer learning approach & PPG & BPs [12] \\ A novel neural network model for BP estimation using photoplethysmography & PPG & BPs [15] \\ A neural-network-based method for continuous BP estimation from a PPG Signal & PPG & BPs [16] \\ Joint regression network for cuffless BP estimation & PPG & BPs [14] \\ BP estimation from ECG using machine learning & ECG & BPs [20] \\ Machine learning with feature extraction for BP estimation using PPG & PPG & BPs [21] \\ A transfer learning for personalized BP estimation using PPG & PPG & BPs [22] \\ A long short-term memory and convolutional neural network using cuffless BP estimation & PPG, ECG & BPs [23] \\ Multichannel-PPG-based SVM for continuous BP estimation & PPG & BPs [19] \\ Cuffless BP monitoring system based on pulse arrival time & PPG, ECG & BPs [24] \\ Real-time cuffless continuous BP estimation using deep learning & PPG, ECG & BPs [25] \\ ECG and PPG features for cuffless blood pressure estimation using machine learning, & PPG, ECG & BPs [30] \\ Cuffless high-accuracy calibration-free BP estimation using pulse transit time & PPG, ECG & BPs [28] \\ Cuffless continuous BP estimation from pulse morphology of photoplethysmograms & PPG, ECG & BPs [27] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the BP estimation literature where BPs denote systolic and diastolic blood pressures, CIs are confidence intervals to represent an uncertainty [4].  
algorithms were used to select weighted features from the original features, as shown in Figure 1d. Thereafter, the adaptive AIC determined the best feature subset as a model selection problem using likelihoods based on the GP algorithm, as shown in Figure 1e. Finally, the best feature subset was used as the input feature for the GP algorithm.