## 1 Introduction  
Currently, many wearable devices make use of the photoplethysmography (PPG) to measure some physiological parameters, such as as heart rate [1], blood pressure [2], oxygen saturation [3],and left ventricular ejection time (LVET) [4]. However, motion artifacts and other different types of noise will easily corrupt the PPG signals, especially when these devices are used in an intense physical activity. When the corrupted PPG signals are acquired and processed to measure certain specific physiological parameters for medical decision, the accuracy of these parameters would be reduced. This result would let physicians make some serious consequences. Unfortunately, the PPG signal is often measured under a motion status for the most PPG applications [5]. Due to this reason, some signal processing methods, such as the independent component analysis [6], empirical mode decomposition [7], wavelet transform [8], and adaptive filtering [9], have been employed to detect or reduce the motion artifacts from the corrupted PPG signals. It is found that the undesired motion artifact is overlapped on the dominant frequency components of the PPG signal [10]. This is a big challenge to process the overlapped phenomenon when the PPG signal was used to measure the physiological parameters. In a real application, the accuracy of the different physiological parameters measured from the PPG signal would be affected according to the types and degrees of motion artifacts; for example, the heart rate measurement needs the accurate main peak of the PPG signal, and the oxygen saturation measurement needs the accurate amplitude of the PPG signal. However, these types of motion artifacts usually depend on the sensor's placed position, body gesture, respiration rate and involuntary tremor. So, a comprehensive approach to assess the quality of the PPG signal for measuring physiological parameters needs to be addressed. Since the PPG signal represents the change of blood volume in the peripheral artery, its waveform bears regular morphological characteristics [11; 12]. The quality of the PPG signal will affect the accuracy of physiological parameters detected by the PPG sensors [13; 14]. Therefore, when the PPG signals are used to measure these parameters, the value of its signal quality index (SQI) may be used to quantify the corruption of PPG pulses. [PERSON] et al. have employed the fuzzy rules to determine the SQI [15], and [PERSON] et al. have applied the characteristics of PPG waveform and the decision tree to classify the SQI [16]. [PERSON] et al. have used the Bayesian hypothesis testing method to analyze the SQI [5]. However, these techniques all require manually setting the thresholds of characteristics of the PPG pulse to classify its quality. [PERSON] et al. have proposed a non-invasive measurement technique to measure the cardiac hemodynamic parameters using the impedance cardiography (ICG) method [17]. High-frequency current signals are inputted through the surface electrodes to detect changes in the capacitive impedance of the thoracic cavity caused by heart beats in order to assess the stroke volume (SV) [18]. In the SV calculation with the ICG technology, LVET is one of important parameters that is measured by the ICG signal. However, when the ICG signal has a low signal-to-noise ratio and motion artifacts, the accuracy of LVET is frequently reduced. [PERSON] et al. have used a PPG sensor placed on the neck to measure the LVET [4]. Their results show that the LVET measured by a good PPG signal is more accurate than that by a good ICG signal. Therefore, they manually select the good PPG pulses to measure the LVET. According to previous mentions, the higher the SQI of the PPG signal, the more accurate the physiological parameters measured by the PPG signal. Thus, the purpose of this study is to develop a new SQI algorithm to access the quality of PPG pulse. The PPG pulse with the higher SQI would be utilized to measure the cardiac SV. This SQI algorithm fundamentally consists of pre-process and post-process steps.