# A Survey of Photoplethysmography and Imaging Photoplethysmography Quality Assessment Methods
## 3 Introduction to Signal Quality Index for Photoplethysmographic Signals
### Applications
#### 3.2.1 Keeping or Removing PPG Signals  
The main reasons to remove a set of PPG signals are as follows:  
* Avoiding misinterpretation and false alarms from corrupted signals;
* Maintaining the performance of an application by keeping only relevant signals;
* Preparing an artifact-free dataset for the training and testing of a machine/deep learning estimator to achieve good performance [60].  
In the case of cleaning, a signal quality assessment algorithm can be used as a tool to indicate which signal should be kept and which should be suppressed (see Figure 5).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**References** & **Details** & **Annotation Type** \\ \hline
[35] & Evolution of PR between 2 pulses and sudden evolution of the value of the blood oxygenation. & \\ \hline
[35; 36; 37; 38] & Comparison between PR estimated from the PPG signal and the HR estimated from ECG. & \\ \hline
[39; 40] & Comparison between stroke volume measured from cPPG and a reference taken from impedance cardiography. & Automatic annotation \\ \hline
[40] & Comparison of left ventricular ejection time measured by a reference with the one measured by cPPG. & \\ \hline
[41] & Labelling by computing already existing quality indicators. & \\ \hline
[36; 42; 43; 44; 45; 46; 47; 48; 49; 50] & Not Guided & \\ \hline
[51; 52; 53; 54] & Guided & By rules \\ \hline
[55; 56] & By reference signal & Manual annotation \\ \hline
[35; 57; 58; 59] & Perturbations artificially created during recording & \\ \hline \hline \end{tabular}  
* PR = Pulse Rate, HR = Heart Rate, ECG = Electrocardiogram, cPPG = Contact Photoplethysmography.  
\end{table}
Table 3: Classification of papers addressing SQI according to the annotation type.  
An SQI algorithm can be evaluated for its contribution to the performance of the estimation. This kind of evaluation is application-oriented. However, it may not represent accurately how the SQI algorithm behaves as it does not consider the proportion of good signals unnecessarily removed or the proportion of bad signals still present after cleaning.  
Some applications need a more accurate evaluation of the signal. For example, in the case of false alarm reduction, it in necessary to separate a corrupted signal from an episode of arrhythmia. This second method relies on an annotated database. The performance of the SQI algorithm is evaluated by comparing the estimated signal quality with the annotated reference.  
The SQI evaluation metrics are usually taken from a confusion matrix (accuracy, sensitivity, precision, positive predictive value (PPV), Area Under Curve-Receiver Operating Characteristic (AUC-ROC)). Accuracy gives the global performance of the SQI algorithm, while sensitivity and precision focus, respectively, on the proportion of signals well classified within the real bad-quality signals and on the proportion of correctly estimated samples in all estimated bad-quality signals. AUC-ROC is the area under the curve in the graph relating the sensitivity and false positive rate obtained by moving the threshold between the good and bad signals. An algorithm with an AUC close to 1 is able to separate the good from the bad signals.