## 1 Introduction  
The photoplethysmographic (PPG) signal has been widely used to measure many physiological parameters, such as pulse rate [1], blood oxygen saturation [2], blood pressure [3], respiration rate [4], and left ventricular ejection time (LVET) [5]. The noninvasive techniques for photoplethysmography include two optical types, transmission and reflection [6], as shown in Figure 1. A light-emitting diode (LED) is often used to generate low-intensity infrared light on the skin, and a portion of the light will be absorbed mainly by both arterial and venous blood. For the reflection PPG, the nonabsorbed light will be reflected and detected by a photo diode. The LED and photo diode are placed on the same side, as shown in Figure 1a. For the transmission PPG, the nonabsorbed light will be transmitted and detected by a photo diode. The LED and photo diode are placed on the opposite side, as shown in Figure 1b. In either the reflection or the transmission method, the PPG signal represents the changes in blood volume (Figure 1), although it cannot be used to quantify the amount of blood. The PPG signal measured by the reflection method is more easily corrupted by the motion noise than the transmission method. Currently, the wearable device with the PPG sensor usually uses the reflection method to perform the physiological measurement. When the users wear these devices to do the exercise, PPG signals are very vulnerable to motion artifacts. The common solution is to improve device mechanisms to reduce the motion effect on the recoded PPG signal and to raise the measured accuracy. However, the higher the quality of the PPG signal, the better the accuracy of the measured parameters extracted from such a PPG signal. Therefore, how to classify the quality of PPG signals is an important issue for the development of wearable devices. PPG is a noninvasive optical measurement method in which the change of blood volume interconnects the physiological responses to circulatory events in peripheral blood vessels. Thus, its waveform bears regular morphological characteristics [7,8]. As shown in Figure 2, there are a lot of physiological characteristics in a PPG pulse, including the main peak, dicrotic notch, pulse width, and amplitude, and so on. As a result, many researchers have used those significant characteristics (i.e., the rule-based methods) to determine the quality of each PPG pulse. In addition, the signal quality index (SQI) represents the corrupted degree of the PPG pulse. [PERSON] et al. have employed fuzzy rules to determine the SQI of PPG pulses [9], and [PERSON] et al. have applied the characteristics of PPG waveform and the decision tree to classify its SQI [10]. [PERSON] et al. have used the Bayesian hypothesis testing method to analyze the SQI [11]. In these studies, they all needed to adjust the thresholds of the rule-base method to get the best results. Recently, [PERSON] et al. used a fuzzy neural network to evaluate the SQI [12]. Although they used the artificial intelligence method to gauge the quality of the minorly corrupted PPG pulse, the rule-base method was also used to delete the majorly corrupted PPG pulses. The traditional approach for the SQI assessment is to extract the features from the PPG signal. It is well known that the morphological approach is sensitive to signal noise and has many limitations on the performance robustness of the classification model [8]. Currently, deep learning techniques have been used to process feature extraction tasks by convolution computation [13]. As the  
Figure 1: Schematic illustration of the photoplethysmography. The LED illuminates the skin and the nonabsorbed light will be detected by the photo diode. (**a**) For the reflected method, the LED and photo diode are on the same side. (**b**) For the transmitted method, the LED and photo diode are on the opposite side.