# A Survey of Photoplethysmography and Imaging Photoplethysmography Quality Assessment Methods
## 5 Design of SQI for iPPG  
### Existing Studies  
Imaging PPG is the photoplethysmography measurement using a camera. Several reasons motivate research in this technology:  
* A camera is a widely used technology allowing remote and continuous monitoring of physiological variables (e.g., PR, PRV); \begin{table}
\begin{tabular}{c c c c c} \hline
**Reference** & **Data** & **Entry** & **Deep Learning Architecture** & **Performance** \\ \hline \multirow{4}{*}{[67]} & 19 subjects & 30 s PPG signal + index & 3 CNN + 2 dense layers & Acc = 0.9002 \\  & 1443 records of 30 s length & indicating motion detected by the smartwatch accelerometer & & AUC = 0.9521 \\  & & injected into the dense part of & & \\  & & the network & & \\ \hline \multirow{4}{*}{[38]} & 5 days data collection & 60 s signal window & 1 CNN + dense network & Best performance: \\  & & & & AUC = 0.88 \\  & & & & Pre = 0.7674 \\  & & & & Sen = 0.8354 \\ \hline \multirow{4}{*}{[55]} & 2 private datasets & 2 entries are tested: & 1D entry: & Best performance (ResNet18): \\  & & 1D entry, 30 s of normalized & Attention LSTM ending with & Acc = 0.9851 \\  & & PPG signal & dense network & Spe = 0.9791 \\  & & 2D entry, normalized RGB & Fully connected network & Sen = 0.9877 \\  & & plot image of the signal & 2D entry: & \\  & & VGG19, Resnet18, Resnet50, & & \\  & & Xception & & \\ \hline \multirow{4}{*}{[40]} & 14 subjects & 2D images of the plot of & ResNet50; VGG19 & Best performance (ResNet50): \\  & & zero-padded PPG pulse and & & Acc = 0.94 \\  & & its derivative & & Pre = 0.96 \\  & & & Sen = 0.92 \\ \hline \multirow{4}{*}{[49]} & 183 subjects & Raw PPG signal & 1 CNN + 1 shared CNN & - \\  & & & + 2 separated CNN with & \\  & & & dense layer & \\ \hline \multirow{4}{*}{[91]} & 38 subjects & 5 s of normalized PPG signal & 13 layers 1D CNN & Acc = 0.945 \\  & BIDMC and ICU dataset from & & & Sen = 0.967 \\  & MIMIC & & & Spe = 0.904 \\ \hline \multirow{4}{*}{[54]} & 76 subjects & PPG pulse signal converted to & 2D CNN + 1 dense & Acc = 0.975 \\  & & recurrence plot & & Sen = 0.964 \\  & & & Spe = 0.987 \\ \hline \multirow{4}{*}{[41]} & PPG : & 5 points window of 64 Hz & 3-unit LSTM + dense layer & Average Acc = 0.7973 \\  & DaLiA dataset [94] and & PPG signal & & \\  & Cuffless Blood Pressure & & & \\  & Estimation dataset [95] & & & \\  & iPPG: 6 subjects & & & \\ \hline \multirow{4}{*}{[92]} & 2 subjects & Discrete wavelet transform & Autoencoder used & Pre = 0.90 \\  & + data augmentation & (DWT) approximation & Encoder : 1D CNN & Sen = 0.95 \\  & & coefficients from second level & + bidirectional LSTM layer & \\  & & of 6 s PPG signal & Decoder : LSTM layer & \\  & & + dense layer & \\ \hline \multirow{2}{*}{[56]} & 44 subjects & 30 s bandpass-filtered signal & 1D U-Net (5 residual encoder, & Mean performance \\  & & & 5 residual decoder) & DICE score = 0.8734 \(\pm\) 0.0018 \\ \hline \end{tabular} Acc = Accuracy, CNN = Convolutional Neural Network, LSTM = Long Short-Term Memory, Pre = Precision, Sen = Sensitivity, AUC = Area under Curve, PPV = Positive Predictive Value.  
\end{table} 
Table 7: Summary of deep learning-based quality assessment algorithms. * This technology imposes less constraints on the patient as the signal can be taken remotely;
* Several potential applications exist for telemedicine (e.g., PR, blood pressure, blood oxygenation). However, this kind of measurement is done in a less controlled environment [96] and is also subject to measurement perturbations (see Table 1). Even if iPPG is still an emerging technology, signal quality indexes have been used to guide the extraction methods implemented to retrieve the signal from the camera [63; 97; 98]. [PERSON] et al. [99] compared the quality of their iPPG signal extracted using periodic variance maximization [97] after filtering it by a bandpass filter, a wavelet filter, or a deep learning architecture using SNR and a template matching method [70] as metrics. [PERSON] et al. [41] designed a 1D signal quality index for PPG and iPPG using a deep LSTM architecture.