# PPG-Based Systolic Blood Pressure Estimation Method Using PLS and Level-Crossing Feature
## 3 Results  
In our method, the PLS regression model was developed on the basis of six scores, which was the optimum number of scores determined after applying [PERSON]'s R criterion [18] to the training data for 180 subjects. This PLS regression model consists of mean values for centering the LCFs and the regression coefficients. This PLS regression model was provided in Table 1 of Appendix A. The conventional MRA model was established for comparison method 1, considering that there were only four features (b/a, c/a, d/a, and e/a). In addition, the correlation coefficients of the features were  
Figure 4: LCF extraction. (**a**) Number of crossings between a waveform and a contour line (LCF 1); in this figure, the number of filled circles on the \(-30\%\) contour line is two. (**b**) Total length of each contour line lying within the waveform (LCF 2); in this figure, the length of the horizontal bold line on the \(0\%\) contour line is \(0.5\). not significantly high: the highest correlation coefficient was 0.631. An MRA model was established for comparison method 2 on the basis of stepwise regression using the Akaike information criterion [19]. Owing to the large number of LCFs (76), it makes the calculation of the stepwise method difficult. For this reason, we selected 20 LCFs beforehand, where the absolute value of the correlation coefficient between the features and the SBP was greater than 0.3. Finally, the number of LCFs used in this stepwise method was five. We selected these 20 LCFs as the initial state of the explanatory variable set and selectively calculated the explanatory variable by a forward-backward stepwise selection method with the Akaike information criterion. Finally, five LCFs were selected as the explanatory variables. Figure 5 shows scatterplots of the measured and estimated BPs obtained from our method (LCFs + PLS), comparison method 1 (HRFs + MRA), and comparison method 2 (LCFs + MRA). The correlation coefficients between the estimated and measured values are 0.78, 0.58, and 0.62 for our method, comparison method 1, and comparison method 2, respectively. The standard errors of the estimate are 9.09, 11.76, and 11.20 mmHg for our method, comparison method 1, and comparison method 2, respectively. On the basis of the results, it can be deduced that our method gives the highest estimation accuracy, followed by comparison method 2, and least of all, comparison method 1. Figure 6 shows the results of a Bland-Altman (BA) analysis [20], which we performed to determine the systematic errors of the regression models. Table 1 summarizes the correlation coefficients of the BA plots and the mean and SD \(\times\) 1.96 of the vertical axis of the BA plots. Even though all of the regression models have negative correlation coefficients, our estimation model has the weakest correlation. The broken line at the center of the three broken lines drawn in each plot in Fig. 6 is the mean of this difference. This mean shows the bias between the SBP measured by the cuff and the SBP estimated from PPG. The bias between the measurement of the SBP by the cuff and PPG is very small in any case. Moreover, the broken lines above and below each plot represent the limit of agreement (LOA). This LOA was determined by \(\pm\)SD \(\times\) 1.96 of the difference above and below, centered on this average. A narrower range for the LOA means that the BP estimation method works more equivalently to the cuff measurement method. The upper side of each LOA was 17.64, 23.04, and 20.88 mmHg. The lower sides were \(-\)18.19, \(-\)23.33, and \(-\)23.31 mmHg. The total number of subjects was 265; therefore, the SDPPG was determined for 5919 pulses on the basis of 20-s measurements for each subject. On the basis of waves a, b, c, d, and e in the SDPPG waveform, the number of pulses in which one or more HRFs were missing was 312. Hence, the percentage of cases in which features could not be extracted was 5%. The percentage of pulses in which all of the HRFs within one record could be extracted was less than 20% for four subjects.