## 4 Experimental Results
### Evaluation Protocols  
The selected feature subset was then randomly split into 80% for training and 20% for testing. Subsequently, systolic reference BPs and diastolic BPs were calculated from the ABP envelope signals, as shown in Figure 2\(c\). First, the mean absolute error (MAE) and standard deviation of the error (SDE ) of the GFSDP algorithm were compared with those of the conventional algorithms to evaluate the experimental results, as shown in Table 7. The probability of the British Hypertension Society (BHS) protocol [46] was also obtained based on the results of the MAE and SDE, as shown in Table 8. The average error of the GFSDP algorithm was calculated by \(\sigma i_{i}=(\sigma p_{i}-rp_{i})\) for each record \(i\), where \(ep\) denotes the estimated BPs (SBP or DBP), and \(rp\) is a reference BP. Thus, the mean error (ME) and MAE were given as \((\frac{1}{n}\sum_{i=1}^{n}er_{i})\) and \((\frac{1}{n}\sum_{i=1}^{n}|er_{i}|)\), respectively. The results were obtained as the average of the 30 experiments for each algorithm. Second, the ME and SDE of the ME were used [10], as shown in Table 9. The ME and SDE between the estimated and calculated reference BPs were calculated using the recommendations of the Association for the Advancement of Medical Instrumentation (AAMI) protocol [47]. A device passes the AAMI protocol if its measurement error has an ME value of less than 5 mmHg with an SDE of less than 8 mmHg [47]. Table 10 lists the CI results of the cuffless BP signals. The CIs obtained using the proposed algorithm were lower than those obtained using the conventional algorithm for acquiring CIs using an SVM with a Student's t-distribution (SVM\({}_{\text{ST}}\)) [8]. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Algorithm** & **ANN** & **MLR** & **SVM** & **LSTM** & **GP** & **GFSDP** \\ \hline Time (s) & 1.67 & 0.13 & 0.37 & 7.48 & 3.84 & 5.56 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Compared feature training and testing times between the proposed and conventional methods based on hardware (Intel®Core(TM) i5-9400 CPU 4.1 GHz, OS 64 bit, RAM 16.0 GB), and software (Matlab®2022 (The MathWorks Inc., Natick, MA, USA)) specifications. \begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline
**Methods** & \multicolumn{2}{l}{**SBP**} & \multicolumn{2}{l}{**DBP**} & \multicolumn{2}{l}{**SBP/DBP**} \\  & **Mean Absolute Difference (\%)** & \multicolumn{2}{l}{**Mean Absolute Difference (\%)**} & \multicolumn{2}{l}{**BHS**} \\  & \(\leq\)**5 mmHg** & \(\leq\)**10 mmHg** & \(\leq\)**15 mmHg** & \(\leq\)**5 mmHg** & \(\leq\)**10 mmHg** & \(\leq\)**15 mmHg** & **Grade** \\ \hline ANN & 26.75 & 49.65 & 68.35 & 39.35 & 72.39 & 88.97 & C/C \\ MLR & 29.45 & 49.04 & 65.94 & 47.89 & 79.71 & 87.42 & C/C \\ SVM & 30.64 & 54.47 & 70.96 & 54.61 & 79.48 & 88.13 & C/C \\ LSTM & 34.20 & 60.07 & 73.16 & 48.65 & 79.48 & 90.65 & C/C \\ GP & 36.30 & 62.07 & 76.56 & 48.65 & 80.71 & 90.71 & C/C \\ GFSDP & 49.49 & 73.01 & 84.97 & 60.26 & 85.39 & 93.74 & C/B \\ \hline Grade A & 60 & 85 & 95 & 60 & 85 & 95 & [46] \\ Grade B & 50 & 75 & 90 & 50 & 75 & 90 & \\ Grade C & 40 & 65 & 85 & 40 & 65 & 85 & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: We used the results of the ANN, MLR, SVM, LSTM, GP algorithms and the proposed GFSDP algorithm to grade the algorithm based on the BHS standard [46], where each result represents the average of 30 experimental data.