# Continuous Camera-Based Premature-Infant Monitoring Algorithms for NICU
## 5 Results
### Respiration Calculator Block  
We could not use the same dataset for respiration that we used for pulse because the ECG recordings were used far less than the pulse oximeter, therefore we had less referenced data. As a result, we created another dataset from our database, where pulse and respiration references were both recorded. For the evaluation we used 2 h of raw data with respiration reference recorded about 3 independent babies. The performance of the respiration network was evaluated on two levels. First, the respiration signal provided by the impedance of the ECG electrodes was compared to the respiration signal calculated by the Respiration Extractor. Though both signals reflect the movement of the chest during the respiration, their representation is different, therefore we cannot expect a perfect match. Moreover, they react to body/limb motion in different ways. The comparison of the curves is illustrated in Figure 13. The statistical evaluation of the two signals can be expressed in MAE (0.5494) and RMSE (0.6768) that can be measured between the signals. The second level of evaluation was on the rate level. The graphical comparison of our solution to the reference is shown in Figure 14. As can be seen, the calculated rates follow the reference, and the differences are mostly where there are extreme rate changes or extreme values occurring. During these periods, there is typically some disturbance which misleads either the reference or the visual methods (or both). The statistical evaluation of the calculated breath rates is shown in Table 4. Here we show the statistics for the entire data set (upper rows) and for some quiet periods (lower rows). The evaluation of rates was performed using 120 s long moving average window on the rates for the fair comparison. This length was used in [5] whom we compared our results with, see Table 4. We also provide Bland-Altman (Figure 15) plot of breathing rates calculated by the classical method which provided better results. Figure 12: The estimated pulse rates from the output signal of PhysNet model-in the case of classical image augmentation and additional frequency augmentation–and the corresponding reference pulse-rate values on a selected time segment. The training of the model benefits significantly from frequency augmentation. We statistically evaluated the contribution of the ROI Detector as well. As is shown in Table 5, the torso detection reduced the MAE. However, UNET increases performance most significantly in situations when limb movement occurs, so it has a smaller effect on the overall MAE and RMSE. An advantage of our algorithm is that we can use a 10-seconds and a 120-s long calculation window simultaneously in real time, the former to recognize apnea situations and respond quickly if it is necessary. The authors in [5] used only a 120-s window, and though it smooths the peaks (hence reducing RMSE), it can respond only slowly to an apnea situation. As we can see in Table 4, the type of peak detection method also has a big effect on the result. This phenomenon is caused by the fact that the extracted respiration signal usually contains special artifacts which are identified as valid peaks by findpeaks, because they are in the valid frequency range and not filtered out by the horizontal threshold. The evaluation time of a 10 s (200-frame-long) video segment is 1120 ms without the ROI Detector (as the latter is not executed for every frame). This can be divided into two parts: the running time of the Respiration Extractor (720 ms) and Rate Calculator Head (400 ms). As we use the CalcRate rather than the Rate Calculator Head, the total execution time is only 720 ms on a PC (CPU: Intel(R) Xeon(R) CPU @ 2.20 GHz, CPU family-6).