# A Survey of Photoplethysmography and Imaging Photoplethysmography Quality Assessment Methods
## 5 Design of SQI for iPPG
### Existing Studies
They highlight the difficulty of designing deep learning methods for iPPG quality assessment as the proportion of bad-quality signals is important in iPPG, which unbalances their training dataset. Moreover, the training of deep learning architectures is difficult as the number of iPPG datasets available for research is limited. The authors augmented their initial dataset with artificial data to train their model. [PERSON] et al. [98] considered the idea that an iPPG signal extracted from different regions of the face may have different qualities. The authors introduced unsupervised superpixel segmentation, to segment the image of the face of a patient into different regions of interest. The final iPPG signal is obtained by summing the PPG signals extracted from each superpixel weighted by their respective SNR. [PERSON] et al. [61] extended the work of [PERSON] et al. [98] by lowering the SNR complexity. The authors first trained a Hidden Markov Model (HMM) to build a probabilistic distribution modeling the evolution of an iPPG signal. The authors also created a HMM to model the noise of the signal. The SNR is then computed as the ratio between the likelihood that the evolution of the signal is explained by the HMM modeling the iPPG signal and the likelihood that it is explained by the HMM modeling the noise. [PERSON] et al. [100] used the average value of the absolute difference between the tracked forehead pixel amplitude at frame \(n\) and \(n-1\). The authors observe sudden increases in its value when a perturbation, such as motion or an illumination change, occurs. They also set different strategies of artifact detection according to the iPPG extraction method. The authors added an additional step for iPPG signals extracted by POS [62] and SSR [64] methods (as these methods are more resistant and react differently to perturbations) by detecting if a change in the amplitude of the signal (characteristic of a corrupted signal) happens in a suspicious portion of the signal. Tables 8 and 9 provide respectively a summary of the features used to design SQIs for imaging PPG and of the existing methods. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scale** & **Domain** & **Features** & **References** \\ \hline \multirow{5}{*}{Pulse} & \multirow{5}{*}{Temporal} & \multirow{5}{*}{Amplitude} & Difference between systole and diastole (pulse amplitude) & [101] \\ \cline{3-4}  & & & Amplitude before and after perturbation & \\  & & & for iPPG extracted by POS or SSR & [100] \\ \cline{3-4}  & & & Template matching [70] & [99] \\ \hline \multirow{5}{*}{Signal} & Temporal & Std of the signal & [64; 101] \\ \cline{2-4}  & Spectral & \multirow{5}{*}{SNR (from frequency spectrum)} & [63; 97; 98; 99; 101; 102] \\ \cline{3-4}  & & Relative difference between highest and second highest amplitude of the & \\ \cline{3-4}  & & signal spectrum & [101] \\ \cline{3-4}  & & Maximum scalar product between PPG periodogram [103] and predefined filters & [104] \\ \cline{3-4}  & & SNR (obtained using HMM models) & [61] \\ \cline{3-4}  & HMM = Hidden Markov Model. & & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Features used for the evaluation of iPPG. iPPG and cPPG share common properties such as semi-periodicity and the extractable features of their respective pulses. Some SQIs can be either used for cPPG or for iPPG. For example, approaches based on the signal to noise ratio [51; 98] can either be applied to cPPG or iPPG signals. The difference lies in the threshold value separating good from bad signals. Some deep learning methods can either be applied to iPPG or cPPG. For example, [PERSON] et al. [41] developed a single LSTM architecture for the assessment of cPPG and iPPG signals. Both types of signals can be analyzed with SQI developed for 1D signals. The case of iPPG is particular as the signal is computed from images. SQI applied to iPPG can be computed on two-dimensional matrices, thus allowing one to analyze subregions of the image [98].