# Continuous Camera-Based Premature-Infant Monitoring Algorithms for NICU
## 6 Discussion  
In the previous section, we showed the accuracy of our methods. In this section, we compare our algorithms with state-of-the-art methods from the literature. The Top-Level  
Figure 14: Comparison of the reference breathing rate and the breathing rates calculated by the Respiration Calculator Block. Every point is related to a 10-s-long input window of frames. Figure 15: The Bland–Altman plot calculated for the error of OpticalFlow + CalcRate. \begin{table}
\begin{tabular}{c c c c} \hline
**ROI Detector** & **Signal Extraction** & **Rate-Calc-Method** & **MAE** \\ \hline UNET & OpticalFlow & calcRate & 4.817 RPM \\ \hline None & OpticalFlow & calcRate & 5.153 RPM \\ \hline \end{tabular}
\end{table} 
Table 5: Statistical evaluation of the ROI detector. Classification block is compared with [31] mostly at feature level. However, the pulse and breathing rate calculation blocks are compared at performance level. In these cases, we have implemented two state-of-the-art published methods [5; 19] and fed them with our datasets. In this way, we could do a fair comparison, because the evaluation of the methods was done on exactly the same data sets. The Top-Level Classification Block detects the presence of the infant and its activity with high confidence. However, higher misclassification rates can be found between classes i, ii, and iii (Figure 7). This confusion is a consequence of the manual, and therefore slightly subjective annotating process, as it is not always possible to define explicitly the boundaries of categories (e.g., whether an infant is moving slightly or significantly). The confusion between care and other classes is identified as a result of the interpretation of the manual tagging policy (similarly to [31]). The human annotators marked care periods as uninterrupted, couple of minutes long time-windows, which sometimes included shorter periods of empty incubator or situations when there were temporarily no adult hands in the incubator. These short periods are detected correctly by the algorithm, hence causing error in the evaluation. We did not include the apnea detection (class iv.) to this evaluation, because here we used only real data, and we do not have real measurement data for apnea situations in our data base at the time of the manuscript preparation. As a comparison to the latest similar role system, our classification methodology gives not only quality assessment as [31], but provides distinction of several typical motion types, broader artifact detection capabilities and intervention types with similar or higher f-score [39] and lower computational effort. The state-of-the-art MAE score of pulse rate is around 6 BPM measured in adults [19] (i.e., on the MAHANOB-HCI publicly available data set [40]). We applied the PhysNet [19] network on our data set comprised of premature-infant subjects with similar results (see Table 3 bottom part)-indicating that similar performance can be achieved in the case of infants despite the fact that their pulse is weaker and faster. This can be (partly) attributed to the fact that not only their head but their torso also holds valuable pulse information which is used by the neural network. We fused and trained PhysNet together with our RateEstNet network (PhysNet + RateEstNet in Table 3) which resulted in better overall performance compared to PhysNet. In ideal conditions their performance was very similar indicating that our method is more robust to environmental factors. The performance of the Respiration Calculator Block was compared with [5] in which the authors investigated specifically the respiration estimation of premature infants in NICU from videos. In that work, the authors applied the DeepFlow [32] method for motion matrix calculation and used Principal Component Analysis (PCA) to find the strongest motion component. We implemented the described algorithm using an open source OpenCV [41] implementation of DeepFlow and scikit-learn for PCA. We calculated the rates for our data sets and compared them to our results.