# A Survey of Photoplethysmography and Imaging Photoplethysmography Quality Assessment Methods
## 4 SQI for cPPG
### Machine Learning  
As with rule-based algorithms, their performance is dependent on the features selected and the algorithms implemented to extract them. Selecting the features for quality level separation adaptable to PPG signals from different datasets needs further design effort. [PERSON] et al. [69] extracted 26 features from the period-domain short-time Fourier transform and performed feature selection via normalized mutual information. This technique shows how knowing the value of a feature reduces the uncertainty in the prediction of the quality of the signal while keeping only features independent from each other. In this study, they extracted four temporal and four periodic features. [PERSON] et al. [46] focused their efforts on the computation of features that would not be affected by the change of PPG device. The authors first computed reference values of features and templates with the first few seconds of each record. Then, they used them to normalize the features computed from the signal and feed machine learning models to predict the quality. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Data** & **Performance** \\ \hline \multirow{3}{*}{[68]} & 13 subjects & Cohen’s \(x=0.64\) \\  & 8 records of 1 min per subject & Sen = 0.89 \\  & & Spe = 0.77 \\  & & Acc = 0.83 \\ \hline \multirow{3}{*}{[58]} & 24 subjects & Acc = 0.888 \\  & \(>\)134 min of recording & Sen = 0.869 \\  & & Spe = 0.983 \\ \hline \multirow{3}{*}{[43]} & Capnobase [83] & 90\% of the signals with artifacts had a score below 95/100 \\  & Complex System Laboratory [84] & below 95/100 \\  & & 14\% of good-quality signals are labelled as artifacts for a score below 85/100 \\ \hline \multirow{3}{*}{[65]} & MIMIC II [82] & Evaluation by the impact on false alarm reduction with suppression \\ \hline \multirow{3}{*}{[70]} & Physionet/Cinc 2011 & Sen = 0.91 \\  & + author-collected database & Spe = 0.95 \\ \hline \multirow{3}{*}{[44,45]} & 63 subjects & Performance of extended algorithm: \\  & 31.5 h of annotated signals & Acc = 0.984 \\  & & Sen = 0.995 \\  & & Spe = 0.916 \\  & & Fre = 0.986 \\ \hline \multirow{3}{*}{[77]} & Capnobase [83] & Performance on Capnobase \\  & Complex System Laboratory [84] & for a threshold of 0.8 \\  & & Sen = 0.9664 \\  & & PPV = 0.9926 \\ \hline \multirow{3}{*}{[81]} & 19 subjects (\(>\)5 min per record) & Mean performance (over authors’ dataset): \\  & + PhysioNet MIMIC II & Acc = 0.935 \\  & & Sen = 0.869 \\  & & Spe = 0.902 \\ \hline \multirow{3}{*}{[53]} & 3 subjects & Best performance: \\  & 6 min records & Acc = 0.9258 \\  & & Sen = 0.9297 \\  & & Spe = 0.9218 \\  & & PPV = 0.9225 \\ \hline \multirow{3}{*}{[48]} & 14 subjects & Classification performance: \\  & 158 records of 10 s & Acc = 0.9268 \\  & & Sen = 0.9286 \\  & & Spe = 0.9245 \\  & & Pre = 0.6420 \\  & & F1-score = 0.9353 \\ \hline \multirow{3}{*}{[79]} & Capnobase [83] & Sen = 0.9466 \\  & Complex System Laboratory & PPV = 0.9678 \\ \hline \multirow{3}{*}{[66]} & 15,000 records of 5 s from 3 different devices & Acc = 0.9321 \\  & + MIMIC II [82] + Complex System & Sen = 0.9822 \\  & Laboratory [84] + Wrist [85] + Cup [86] & Spe = 0.9071 \\ \hline \multirow{3}{*}{[72]} & 19,700 segments of 4 s & Acc = 0.9989 \\  & & Sen = 0.9994 \\ \cline{1-1}  & Spe = 0.9939 \\ \hline \hline \end{tabular} Acc = Accuracy, Pre = Precision, Sen = Sensitivity, AUC = Area under Curve, PPV = Positive Predictive Value.  
\end{table} 
Table 5: Summary of the rule-based type of quality assessment algorithms. Contrarily to rule-based techniques, the use of machine learning has the advantage of avoiding statistical studies to design the thresholds separating the different levels of quality. It also allows complex non-linear combination of the signal features. Support vector machine (SVM) is the most commonly used model among publications [36; 46; 69; 87; 88]. It has the ability to non-linearly project the extracted features to a higher-dimensional space to increase the probability of finding a hyperplane separating good from bad signals.