## 7 Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Subject** & **Accuracy** & **Precision** & **Sensitivity** & **Specificity** \\ \hline
1 & 0.95 & 0.98 & 0.91 & 0.99 \\
2 & 0.94 & 0.97 & 0.93 & 0.95 \\
3 & 0.86 & 0.90 & 0.90 & 0.77 \\
4 & 0.83 & 1.00 & 0.79 & 1.00 \\
5 & 0.76 & 1.00 & 0.68 & 1.00 \\
6 & 0.74 & 0.99 & 0.69 & 0.95 \\
7 & 0.77 & 1.00 & 0.76 & 1.00 \\
8 & 0.89 & 0.92 & 0.94 & 0.66 \\
9 & 0.85 & 1.00 & 0.82 & 1.00 \\
10 & 0.91 & 0.99 & 0.89 & 0.98 \\ All pulses & 0.86 & 0.97 & 0.84 & 0.93 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The statistic results for the PPG pulses with the low SQIs for the individual subject and the total subjects. was less than that in the positive (high accuracy) or negative (low accuracy) region in clinical trials. In this study, only 70 of the 1466 PPG pulses belong to the middle SQIs. Alternatively, there are 861 and 535 PPG pulses belonging to the high and low SQIs, respectively. Figure 9 shows a 9-s record of the PPG signal and its differential signal for Subject 5. The data of the first and third rows are the SVs measured by the medis(r) CS 2000 and our ICG device, respectively, and the data of the second and fourth rows are the LVET measured by the medis(r) CS 2000 and our ICG device, respectively. The data of the fifth row are the classified results, in which '1' represents the pulse with high SQI, '2' represents the pulse with middle SQI, and '3' represents the pulse with low SQI. For the first pulse, it belongs to one of the pulses with low SQI because its starting ejection zone does not have a sharp valley. Thus, the LVET measured by the differential waveform is larger than the reference value, 368 ms versus 225 ms. For the fourth pulse, because it has a sharp valley in the starting ejection zone and a clear dicrotic notch, the SV measured with this pulse is very close to the reference SV, 56 mL versus 59 mL. However, this pulse has a serious baseline drift during diastole. Thus, the next pulse belongs to one of the low SQI pulses. For the second pulse, although it has a good sharp valley and a clear dicrotic notch, it results in an error of 9 mL in the SV measurement due to a middle SQI pulse. Therefore, the pulses with the middle SQIs belong to border classification, which are difficult to identify the qualities of these pulses. With respect to the reference, the pulses with middle SQIs offer a statistic error of \(3.5\pm 16.1\) mL, which is very close to that of \(6.4\pm 12.8\) mL for those pulses with high SQI. When the baseline of the PPG signal wandered seriously, our proposed algorithm can ignore this segment, as shown in Figure 10. However, for a PPG pulse with distinct and clean contour, if its baseline is not so badly drifted, then the SQI of such a PPG pulse will be considered high. For instance, although a baseline drift is present in the ninth PPG pulse in Figure 10, it is still considered to be high SQI. The reason is that the LVET is measured from the differential waveform, and the differential signal is similar to a signal filtered by a high-pass filter. Thus, the baseline drift phenomenon in the PPG signal would be reduced in the differential signal. Figure 9: The PPG signal (solid line) and its differential signal (dashed line) for Subject 5, and his SVs and LVET measured with the medis® CS 2000 and our ICG device. It is difficult to precisely determine the SQI level of a PPG pulse only by a rule-based algorithm. In such a rule-based method, the thresholds of waveform parameters are adjusted usually by the researchers' experience. In other words, there is no standard criterion in selecting those thresholds. In this study, we only adopt the rule-based algorithm to classify the segments that have the serious baseline drift. There were 1466 pulses to validate the performance of the proposed algorithm. The performance of our method for the pulses with low SQIs was better than it for the pulses with high SQIs: the sensitivities were 0.84 versus 0.81, and the specificities were 0.93 versus 0.90.