# A Survey of Photoplethysmography and Imaging Photoplethysmography Quality Assessment Methods
## 4 SQI for cPPG
### Deep Learning
\begin{table}
\begin{tabular}{c c c c} \hline
**Reference** & **Data** & **Machine Learning Method** & **Performance** \\ \hline  & 104 subjects, 1055 pulsations & MLP & Acc = 0.952 \\
[42] & & & Sen = 0.990 \\  & & & Spe = 0.806 \\  & & & PPV = 0.952 \\ \hline  & 33 subjects & SVM + temporal neighbor voting & Mean performance on 3 artefacts \\
[35] & & & (finger motion, head motion, walking): \\  & & & Acc = 0.938 \\  & & & Sen = 0.943 \\  & & & Spe = 0.924 \\ \hline  & 15 subjects & C-SVC & Mean performance: \\
[69] & 22 records of 1 min per subject & & Acc = 0.885 \\  & & & Sen = 0.843 \\  & & & Spe = 0.915 \\ \hline  & 5 different datasets & SVM & Precision in the detection of the \\
[36] & (Chon Lab and UMass Medical center) & & occurrence time of a MNA \\  & & & Difference in Transit Time = 0.91 \(\pm\) 0.59 s \\ \hline  & 13 subjects & SVM & Acc = 0.9033 \\  & & & Sen = 0.9505 \\
[52] & & & Spe = 0.9163 \\ \hline  & 17 subjects & SVM & Acc = 0.984 \\
[37] & 24 h record per subject & & Sen = 0.8550 \\  & & & Spe = 0.9184 \\ \hline  & 40 subjects & Fuzzy neural network & Mean performance: \\
[59] & records of 1.5 to 2 min & & Acc = 0.8992 \\  & & & Sen = 0.8421 \\  & & & Spe = 0.9363 \\ \hline  & 46 subjects & Test of 3 machine learning models: & Best score from SVM, mean performance: \\
[46] & + Capnobase dataset & Classification and regression tree, SVM, & Sen = 0.9576 \\  & & ensemble tree & Spe = 0.9190 \\  & & PPV = 1 \\ \hline  & 26 subjects & Test of k-nearest neighbor, & Best score for random forest: \\  & & multi-class SVM, Naive Bayes, & Acc = 0.745 \\  & & decision tree, random forest & \\ \hline  & 10 subjects & Fuzzy neural network & Performance on detection of \\
[39] & 3 min per record & & bad-quality tubes : \\  & & & Acc = 0.86 \\  & & & Pre = 0.97 \\  & & & Sen = 0.84 \\ \hline  & 30 subjects & Self-organizing map & Acc = 0.9201 \\
[73] & 10 min per subject & & Sen = 0.9580 \\ \hline  & 5 subjects & Unsupervised elliptical envelope & Results of the leave-one-subject-out test \\
[50] & 12 min of recording per hour for & algorithm & in the classification of bad quality: \\  & & & Pre = 0.85 \\  & & & Sen = 0.98 \\ \hline  & & & \\ \end{tabular} Acc = Accuracy, MLP = Multi-Layer Perceptron, MNA = Motion Noise Artifact, Pre = Precision, Sen = Sensitivity, PPV = Positive Predictive Value, SVM = Support Vector Machine, C-SVC = C-Support Vector Classifier.  
\end{table} 
Table 6: Summary of machine learning methods dedicated to quality assessment. Figure 7: Presentation of several deep learning architectures dedicated to quality estimation of a cPPG signal. (**a**) Architecture used by [PERSON] et al. [54] using 2D convolution to treat each pulse of the signal as an image. (**b**) Architecture implemented by [PERSON] et al. [92]. Because the architecture is only trained on clean PPG signals, it will fail to reconstruct a PPG signal when the input is corrupted, thus increasing the MSE between the reconstructed signal and the input signal. With a manually chosen threshold, it allows one to make the distinction between good and bad signal quality. (**c**) Architecture implemented by [PERSON] et al. [56] showing the estimation of the quality of the signal as a segmentation problem using deep learning. Signal processing is still necessary for the generalization of deep neural networks over several datasets. Moreover, this kind of SQI algorithm needs a large dataset with annotated data for training, which is difficult and expensive in the case of manual annotation (see Section 3.1.3). Automatically annotating datasets with already existing SQI allows one to solve the problem of the number of samples but without any relevant increase in performance. Another main limitation of the use of deep learning is the lack of transparency in the features learned in the PPG signal fed to the DNN. To tackle this issue, [PERSON] et al.