## 5 Discussion  
In a rule-based classification approach, only finite characteristics in time or frequency domains are extracted from a PPG pulse. Therefore, the performance of such a rule-based classification approach depends on the kind and number of characteristics. Since the number of the selected characteristics is always limited, all the information that exists in the PPG pulse is not fully utilized in the rule-based approaches [3; 10; 12]. Essentially, the main characteristics of a high-quality PPG pulse directly affect the measuring physiological parameter. In this study, LVET is defined as the time interval initiated at the opening of the aortic valve and terminated at the closing of the aortic valve. Thus, for the morphology of a PPG pulse, the starting ejection point is the first zero-crossing point of the DPPG pulse during systole. The ending ejection point is the time for the first minimum valley of the DPPG pulse during systole, which happens before the dicrotic notch. Thus, the clear foot and dicrotic notch are the main characteristics of the high-quality PPG pulse. In the 2D DCNN, the convolution layers can automatically classify the different feature patterns from the raw image. Thus, the performance of the 2D DCNN in this study is found to be better than that of our previous study using the rule-based method [12]. In the previous study [5], we found that a substantial error is usually present in the LVET measured by the PPG or ICG, as compared with the standard reference measured by phonocardiography. Although the SV has a linear relation with the LVET according to Equation (1), the SV measured by medis(r) CS2000 is calibrated through some parameters. In this study, both SV and LVET measured by medis(r) CS2000 are used as the references to compare with those measured by our ICG device. In the study, one of our findings is that the application of high-quality PPG pulses leads to relatively lower errors  
Figure 9: The results of SQI classification with the ResNet-50 model for the PPG (blue line) and DPPG (orange line) signals corrupted by serious baseline drift. The first and third rows of the data are the two SVs with medis® CS 2000 and our ICG device, respectively, while the second and fourth rows are the two LVETs with medis® CS 2000 and our ICG device, respectively. The fifth row represents the error percentages of SV. The red line denotes the output value of the ResNet-50 model. The cross and circle symbols represent the first zero-crossing point and minimum-value point of the DPPG pulse, respectively. in the SV and LVET measurement, as shown in Figures 8 and 9. Thus, only the PPG pulse with high quality can be used to obtain a reliable LVET and, subsequently, yield an accurate SV. In Table 5, the SV is measured by high-quality PPG pulses in which the statistic errors of SV for the VGG-19 and ResNet-50 models are found to be relatively low (\(4.5\pm 14.7\) and \(2.6\pm 14.2\) mL), respectively. In previous studies [10; 25; 26], the quality level of a PPG pulse was defined by experts in a manual fashion. However, in the validation of their algorithms, a direct comparison of performance between two published algorithms is restricted due to the different cognitive abilities of such experts. In this study, we use three error percentage degrees, below 18%, between 18% and 20%, and above 20%, to classify individual PPG pulse's SQI (low, middle, or high). Based on the quantitative degrees of error percentage, the proposed algorithm can effectively differentiate the quality level of each PPG pulse. Additionally, the accuracy in the SV measurement with a high SQI PPG pulse classified by the algorithm is found to be higher than that with a low SQI PPG pulse. A classification approach using the DCNN does not need predetermined characteristics or features and makes full use of the information embedded in the PPG pulse by taking advantage of a deep learning process [27; 28]. In our previous study, we proposed a rule-based method combined with a fuzzy neural network to determine the SQIs of PPG pulses [12].