## 3 Results  
Figure 3 shows the ECG and PPG signals. Since each subject performed the experiment to raise blood pressure five times, the dropping times of their blood pressure were different and so were the numbers of measured data for each subject. We used the data for each subject to make develop their personalized blood pressure models, and the leave-one-out cross validation was used to validate the performance of the multi-dimension regression model and the DNN. Figure 4 shows the correlation coefficient, \(r\), between parameters of the pulse wave and the systolic and diastolic pressures. We found that the PTT and HR had the highest correlation with blood pressure. To choose the optimal parameters for estimating blood pressure, we used different parameter combinations to construct the blood pressure models. According to the ranking of the correlation coefficient in Figure 4, we would first delete the parameter with the lowest correlation coefficient. The parameters would be deleted one by one until the last parameter, the PTT. Table 4 shows the \(E_{RMS}\) of the estimated blood pressure for the ten subjects with only the PTT. The \(E_{RMS}\) statistics of were \(8.2\pm 3.00\) mmHg and \(4.5\pm 0.75\) mmHg for the systolic and diastolic pressures, respectively. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Subjects** & **Systolic Pressure (mmHg)** & **Diastolic Pressure (mmHg)** \\ \hline  & Max. & Min. & Max. & Min. \\
1 & \(134\pm 11.3\) & \(105\pm 3.4\) & \(81\pm 3.3\) & \(69\pm 4.4\) \\
2 & \(139\pm 4.7\) & \(112\pm 2.8\) & \(78\pm 2.8\) & \(66\pm 3.8\) \\
3 & \(146\pm 8.3\) & \(116\pm 3.8\) & \(81\pm 2.1\) & \(67\pm 3.8\) \\
4 & \(142\pm 5.6\) & \(116\pm 1.1\) & \(81\pm 3.8\) & \(62\pm 5.3\) \\
5 & \(161\pm 17.1\) & \(124\pm 4.3\) & \(83\pm 4.8\) & \(67\pm 0.9\) \\
6 & \(151\pm 12.5\) & \(114\pm 1.1\) & \(78\pm 2.9\) & \(66\pm 4.0\) \\
7 & \(146\pm 6.0\) & \(117\pm 1.7\) & \(69\pm 4.4\) & \(57\pm 3.5\) \\
8 & \(152\pm 4.8\) & \(124\pm 6.2\) & \(93\pm 2.7\) & \(81\pm 7.3\) \\
9 & \(134\pm 8.5\) & \(105\pm 2.5\) & \(72\pm 5.8\) & \(61\pm 3.5\) \\
10 & \(133\pm 3.5\) & \(108\pm 2.2\) & \(74\pm 1.5\) & \(65\pm 1.5\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The statistics (mean \(\pm\) SD) of the maximum and minimum blood pressures of subjects in the five exercises. Since the best first five and six parameters could be used to estimate the better blood pressure, the results of all parameters, the six parameters, and the five parameters are shown in Tables 5 and 6, respectively. In Table 5, the \(E_{RMS}\) statistic with all parameters was \(7.3\pm 3.46\) mmHg, which was better than the \(E_{RMS}\) using the PTT parameter only, which was \(8.2\pm 3.00\) mmHg. When the best first six and five parameters were used to estimate the blood pressure using the linear multi-dimension regression model, the \(E_{RMS}\) statistics were \(7.1\pm 2.60\) mmHg and \(6.9\pm 2.81\) mmHg, respectively. The results were better than the \(E_{RMS}\) statistics of the DNN, which were \(9.5\pm 1.73\) mmHg and \(9.5\pm 1.7  
the \(E_{RMS}\) statistic using only the PTT parameter, which was 4.5 \(\pm\) 0.75 mmHg. When the best six and five parameters were used to estimate the blood pressure using the linear multi-dimension regression model, the \(E_{RMS}\) statistics were 4.0 \(\pm\) 0.70 mmHg and 4.0 \(\pm\) 0.65 mmHg, respectively. The results were also better than the \(E_{RMS}\) statistics of the DNN, which were 4.7 \(\pm\) 0.55 mmHg and 4.7 \(\pm\) 0.58 mmHg, respectively. Figure 5 shows the measured blood pressures and the estimated blood pressures during five exercises for the fifth subject. The subject's systolic and diastolic pressures dropped about 30 mmHg and 16 mmHg, respectively. The accuracy of the estimated diastolic pressures was better than the estimated systolic pressures, and the \(E_{RMS}\) values were 3.4 mmHg and 4.1 mmHg, respectively.