# Continuous Camera-Based Premature-Infant Monitoring Algorithms for NICU
## 5 Results
### Pulse Calculator Block
Figure 9: Frames from our challenging test dataset (#1 in Table 2). Conditions depicted from left to right: Heavy motion-twisting the body, swinging arm, covered baby, normal and low light conditions. Then, a Fourier transform is applied on a sliding window of length 512 samples with a stride of 1 s. A Hamming window is also used on the sliding window, before the Fourier transform to avoid spectral leakage. The maximum power spectrum component is chosen to be the estimated pulse rate. The RateEstNet network is \"fused\" and trained together with PhysNet. The results of the standalone PhysNet and the results with the addition of RateEstNet are depicted in Figure 10 and the Bland-Altman plot of RateEstNet can be seen in Figure 11. From the latter, we can see that there is no significant systematic error (as the mean is close to zero) between the reference and RateEstNet method. Figure 11: The Bland–Altman plot of results of Physnet+RateEstNet and reference values. Figure 10: Illustration of model estimates and reference of pulse rates. At the bottom the whole 15-h-long test dataset is plotted. Above that two selected 2-h-long segments are enlarged. Finally, at the top a half-an-hour-long segment is shown. From the bottom plot we can see that the estimation is the worst from 6.5 to 8 h. The explanation for this is that in this part the head of the neonate is covered with sheet, therefore only a small skin area is visible. From Table 3. it is clear that using the RateEstNet network (the second row in Table 3) after PhysNet is beneficial in estimating pulse rate according to the metrics corresponding to the whole test dataset. On the other hand, -as one can observe in Figure 10. -at specific parts in our test set, the performance of especially PhysNet is severely degraded: from around 6 th to 8 th h where the head of the neonate was covered with a sheet and from 12 th to 13 th h where illumination level was low (see snapshots in Figure 9). Therefore, we also calculated the metrics when omitting these parts shown in the bottom two lines of Table 3. In this way, the performance was very similar, suggesting that the fusion of PhysNet and RateEstNet could better handle the challenging segments while the performance was almost the same outside of them. In the evaluation, we used the rate values provided by Philips IntelliVue as reference. There is a difference in the parameters of the filtering used by Philips and the parameters of the filter used in our algorithm. Moreover, there would be differences in other parameters, thresholds too. Therefore, we cannot obtain a perfect match between the pulse rates and the reference. There are smaller slips between the reference values and the calculated rates, consequently we obtain lower correlation values in the third column. If we used average filtering in the comparison, the correlation values increased over 0.8 and RMSE values were halved. In addition, the effect of frequency augmentation was inspected. The model was better able to handle extreme cases (when pulse rate was lower or higher than average) and resulted in better overall performance when frequency augmentation was applied during training, see a comparison in Figure 12.  
The evaluation time of a 6.4 s video segment is approximately 10 s for the PhysNet network and 0.01 s for the rate estimator network (RateEstNet) on a CPU. We also tested the inference time of the networks on an _NVIDIA Jetson Nano_ embedded system (which has a 28-core Maxwell GPU) to prove that the networks are capable of real-time estimation on a relatively low-price hardware. The running time of the ensemble of PhysNet and RateEstNet is approximately 2 s for a 6.4 s video input, which verifies the ability of the networks to function in real time.