## 5 Discussion
In order to increase the tolerance of the rule-based method, a PPG pulse with an error percentage of SV less than 40% was considered to be of high quality. In the test data, the statistic error of PPG pulses classified to be of high quality was set \(6.4\pm 12.8\) mL. However, the accuracies for successfully determining high- and low-quality pulses achieved only 0.83 and 0.86, respectively. On the other hand, in the present work, we label a PPG pulse as high quality when its error percentage of SV is less than 20%. In the test data, the statistic error of pulses classified as high quality with the proposed ResNet-50 model is \(2.6\pm 14.2\) mL. The accuracies for successfully classifying high- and low-quality PPG pulses are 0.91 and 0.94, respectively. Since the performance of the proposed 2D DCNN approach for the SQI classification seems to be better than the rule-base method, the DCNN method may be applied to increase the measurement accuracy of SV. Moreover, when the PPG signals are corrupted by serious baseline drift, these PPG pulses should be removed by some algorithms before classifying their SQIs using the rule-based method. In the study, the proposed 2D DCNN approaches (VGG-19 or ResNet-50) can make use of the morphologies of PPG and DPPG waveforms to determine their SQIs. The PPG and DPPG signals are first merged and transformed into an image, as shown in Figure 5, before we can use them to perform the classification task. As shown in Figure 5c, the image is constructed by the PPG and DPPG pulses in which the PPG pulse almost lacks the fundamental morphology of a traditional PPG waveform, but it can still be correctly classified as a low-quality one by the proposed ResNet-50 model (Figure 9). This suggests that the proposed 2D DCNN approaches may be useful for quality classification of the PPG pulses, even for those seriously corrupted by motion artifacts and power line interference. It is assumed that in a continuous PPG signal, the morphology of a high-quality PPG pulse may be gradually changed to a low-quality PPG one. Hence, a middle-quality pulse can be considered as a transitional one between the high- and low-quality pulses. In the present work, we define the error percentage of the measured SV with middle-quality pulses to be between 18% and 20%. Therefore, both the ResNet-50 and VGG-19 models are trained only using high- and low-quality PPG pulses, excluding the middle-quality ones. The output layers of the VGG-19 and ResNet-50 models use the sigmoid function as the active function. Thus, the current VGG-19 or ResNet-50 model can be considered as a regression model for determining the morphologic change of the PPG pulse. We define the output ranges of high- and low-quality pulses as between 1.0 and 0.8 and between 0.5 and 0.0, respectively. Of course, some testing pulses may be classified as the middle-quality ones when their testing outputs are between 0.8 and 0.5. Therefore, in Table 4, we test the performance of the VGG-19 or ResNet-50 model with the high-quality class and the not-high-quality class, and with the not-low-quality class and the low-quality class. Although the ResNet-50 model is constructed by a trained DRNN architecture with a 50-layer network, its average accuracy (0.940) for classifying the high- plus middle-quality and the low-qualityPPG pulses is higher than that (0.92) of the VGG-19 model with a 19-layer network. However, both sensitivity (0.915) and specificity (0.92) of the ResNet-50 model are lower than those (0.97, 0.97) of the VGG-19 model for classifying these two quality groups. It seems that due to few samples included in the study, no significant difference exists between the performances of the ResNet-50 and VGG-19 models. There are some limitations to the present study. First, because the subjects recruited in this study all are healthy males, the pulses with the middle- or low-quality are all corrupted, mostly by the motion artifacts. In the study, we did not acquire the PPG pulses belonging to arrhythmic beats.