# Blood Pressure Monitoring Based on Flexible Encapsulated Sensors
## 4 Blood Pressure Measurement
### Results  
The _MAE_ and _SD_ between the preliminary values of systolic and diastolic blood pressure obtained by the arterial tonometry method and those measured by the commercial electronic blood pressure monitor are shown in Table 3. The _MAE_ and _SD_ of the blood pressure values obtained by the different machine learning regression algorithms are shown in Table 4, where AT represents the arterial tonometry method; PWP represents the pulse wave parameter method, which means that the pulse wave parameter method was the feature set obtained by extracting features (excluding the preliminary values of systolic and diastolic blood pressure obtained by the arterial tonometry method) and using differentregression algorithms to obtain the _MAE_ and _SD_ between the true and predicted blood pressure values. AT-PWP represents the blood pressure measurement method proposed in this paper, which combines the arterial tonometry method with the pulse wave parameter method. This method used the preliminary blood pressure values obtained by the arterial tonometry method and other extracted pulse feature parameters as input features and then uses different machine learning regression algorithms to calculate the _MAE_ and _SD_ between the predicted and true blood pressure values. According to Tables 3 and 4, the arterial-tonometry-based measurement method exhibited good measurement accuracy. Combining the arterial tonometry method with the pulse wave parameters method effectively improved blood pressure measurement accuracy. The accuracy of systolic and diastolic blood pressure obtained with this method improved by 31.4% and 21%, respectively, compared with those of the arterial tonometry method. Among them, the use of the random forest algorithm as the regressor achieved the highest blood pressure prediction accuracy. The mean absolute deviation for systolic blood pressure was 3.24 mmHg with a standard deviation of 4.04 mmHg, while the mean absolute deviation for diastolic blood pressure was 4.25 mmHg with a standard deviation of 5.15 mmHg. Therefore, for the blood pressure monitoring system developed in this study, we selected the random forest algorithm as the regressor. To verify the consistency between the blood pressure values measured by the self-made blood pressure monitor and the commercial blood pressure monitor, a Bland-Altman plot was drawn, as shown in Figure 11. The horizontal axis of the plot represents the average of the two blood pressure measurement values, and the vertical axis represents the difference between the two blood pressure measurement values. The solid black line represents the mean difference (mean), and the red dashed lines represent the 95% confidence interval (mean \(\pm\) 1.96 SD). According to Figure 11, there were a few data points for systolic and diastolic blood pressure measured by this system and by the commercial blood pressure monitor that fell outside the limit lines (i.e., the 95% confidence interval). However, the majority of the data points were within the limit lines. Among the 80 sets of test data, the average deviation of systolic blood pressure measured by this system was 0.86 mmHg with a standard deviation of 4.04 mmHg, while the average deviation of diastolic blood pressure was 0.63 mmHg with a standard deviation of 5.15 mmHg, which satisfies the AAMI standard for clinical use (mean deviation \(<\) 5 mmHg, standard deviation \(<\) 8 mmHg).