# A Survey of Photoplethysmography and Imaging Photoplethysmography Quality Assessment Methods
## 4 SQI for cPPG
### Rule-Based
#### 4.1.2 Stability of the PPG Waveform  
Among the reviewed papers, some suggest using the stability property of the waveform of the PPG signal to discriminate between good- and bad-quality signals. Instability of the waveform of a PPG signal's pulsations is likely due to measurement perturbations such as motion artifacts or sensor clipping. To check the stability of a PPG pulse, a common process is to extract the pulses from the PPG signal. Then, each pulsation is compared to a reference, i.e., a model of pulsation built from the signal and called a template. If the pulse is different from the reference, the pulse is considered as low-quality. For example, [PERSON] et al. [70] computed a template as the mean pulsation of a portion of signal neighboring the pulsation to be evaluated, whereas [PERSON] et al. [43] used only the previous pulsation as the template. [PERSON] et al. [76] built a template iteratively within the first few seconds of the signal. The authors first spot an interesting portion of a pulse (with points of interest) and take it to initialize the template. Then, they iteratively use the Toeplitz correlation matrix to find the next portions of pulse aligned with the template and add it to the current template. On this principle, several papers have suggested original methods to build a pulsation model and compare it with the PPG pulse to be evaluated. Building a template of a pulse as the average of detected pulses in the signal [43; 46; 65; 70; 77; 78] or using neighbors [43; 79] is a common method. The difference is based on how the pulses are selected for the building of the template ([PERSON] et al. [65] used all pulses in a 30 s windows, while [PERSON] et al. [43] preselected the pulses by performing normalized cross-correlation) and how the signals are aligned ([46; 77; 78] applied dynamic time warping (DTW) for the alignment of the pulses that [80] used to build the template; [PERSON] et al. [76] first spotted a portion of signal presenting interest points and then iterated over the signal to find aligned portions of signals to add to the template). Comparing the template with new pulses is mainly done with the correlation [43; 65; 70; 79] and the DTW distance [53; 65; 77; 78]. [PERSON] et al. [46] made a comparison using the Kullback-Leibler divergence, while [PERSON] et al. [76] suggested a more original way to compare the signals by first computing a point-to-point difference between the template and the signal and then computing the variance of the obtained signal. If the variance is high, then the quality is low. [PERSON] et al. [81] suggested a comparison with several pulses, in a model called the master template. These templates are obtained from the two first components of a PCA analysis performed on pulses of the MIMIC II dataset [82]. A pulse is then compared with the template maximizing normalized correlation. If the pulse and its neighbors are not labelled as artifacts, then it is used to update the template.  
The rule-based methods present several limitations. As the different conditions are commonly designed manually, another statistical study may be needed when the SQI algorithm is applied to a PPG signal obtained by a different process of measurement (e.g., different device and conditions of measurement). Moreover, the performance of the algorithm of the SQI depends on the signal processing and feature extraction algorithms and should be evaluated along with the SQI algorithm [45]. The reviewed methods are summarized in Table 5.