## 5 Discussion  
This paper proposed a method for estimating oxygen saturation using convolutional neural networks and images acquired with an RGB camera. Because the AC and DC components, which are essentially calculated in the measurement of oxygen saturation, can vary sensitively depending on the calculation method, the time-series data of the image was directly utilized. Furthermore, to account for the significant time required for successful oxygen saturation measurement, experiments were conducted with varying input times. Table 9 summarizes the oxygen saturation prediction results according to the input data lengths. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **Correlation Coefficient** & **MAE** & **RMSE** \\ \hline \multirow{3}{*}{Integrated Model} & 10 s & 0.414 & 2.592 & 3.108 \\  & 20 s & 0.473 & 3.869 & 4.797 \\  & 30 s & 0.475 & 3.025 & 3.547 \\ \hline \multirow{3}{*}{Personalized Model} & 10 s & 0.570 & 1.755 & 2.284 \\  & 20 s & 0.630 & 1.720 & 2.219 \\  & 30 s & 0.663 & 2.142 & 2.612 \\ \hline \multirow{3}{*}{Previous Research [15]} & 0.680 & - & 1.819 \\  & 0.496 & 1.18 & 1.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Summary of the oxygen saturation prediction results according to the input data lengths. Figure 18: SHAP values for the training and testing data of a model trained with 30 s of signals as the input data. First, we trained a unified model using half of the images acquired from the subjects and conducted tests. The results were as follows: when using 10 s of data, the Pearson correlation coefficient was calculated as 0.414, with the MAE (mean absolute error) and RMSE (root mean square error) calculated as 2.592% and 3.108%, respectively. When using 20 s of data, the Pearson correlation coefficient was calculated as 0.473, with the MAE and RMSE calculated as 3.869% and 4.797%, respectively. When using 30 s of data, the Pearson correlation coefficient was calculated as 0.475, with the MAE and RMSE calculated as 3.025% and 3.547%, respectively. This can be considered as not accurately predicting oxygen saturation when compared to previous research. However, studies that measured oxygen saturation non-invasively and individually trained and tested for each subject generally. Therefore, in this study, similar to previous research, we conducted training and testing for each subject separately. As a result, for a 10-second input, the Pearson correlation coefficient was calculated as 0.570, with an MAE of 1.755% and RMSE of 2.284%, with an ICC (intraclass correlation coefficient) of 0.574. For a 20-second input, the Pearson correlation coefficient was calculated as 0.630, with an MAE of 1.720% and RMSE of 2.219%, with an ICC of 0.681. For a 30-second input, the Pearson correlation coefficient was calculated as 0.663, with an MAE of 2.142% and RMSE of 2.612%, with an ICC of 0.646. In comparison to the previous studies [15; 17] that estimated oxygen saturation from the facial region of interest (ROI), their average correlation coefficients were 0.68 and 0.49, respectively, indicating that these prior studies performed well in predicting oxygen saturation. Furthermore, the method proposed in this paper is more efficient as it utilizes the signals obtained from images without the need to calculate additional AC and DC components. In this study, we investigated the factors influencing oxygen saturation estimation by controlling various variables such as illumination, distance, and skin tone through SHAP (Shapley additive explanations) value analysis. Particularly, the analysis utilizing the SHAP values revealed significant variations in the feature importance contributions across different subjects when the trained model predicted oxygen saturation. This finding suggests that there may be diverse sets of features that exert the greatest influence on predictions for each individual.