# Respiration Monitoring for Premature Neonates in NICU
## 4 Experimental Results and Discussion  
Figure 4 shows a visual comparison of the reference breathing signal from the CI and two extracted respiration signals by the proposed optical flow- and deep flow-based methods. The three re-scaled 1D signals from the CI, optical flow- and deep flow-based motion estimations are depicted in Figure 5.  
Figure 3: Framework of deep flow. Figure 4: Monitored respiration from the CI, and corresponding extracted respiration signals from a segment, based on optical flow and deep flow. Figure 5: Data patterns for chest impedance and video-based observation: (**a**) re-scaled 1D signals; and (**b**) zoom in of each interval indicated in (**a**). Table 1 summarizes the estimation of respiration rate using two different optical flow methods. Table 2 shows the root mean-squared errors (RMSE) and cross-correlation (CC) coefficients of the reference breathing signal from the CI, compared to our optical flow- and deep flow-based results. The results obtained from deep flow are more accurate than those of the conventional optical flow approach. The average cross-correlation coefficients of all videos is 0.74 and the average RMSE is 4.55, using the proposed deep learning-based method. Despite the limited number of measurements, these preliminary results provide a promising result for further investigation of the neonatal video-based respiration monitoring method. The overall RMSE for our deep learning-based method (4.55) shows the feasibility to apply our automated respiration for clinical use. From both the correlation and Bland-Altman analyses in Figure 6a,b, the deep flow based-method produces a lower error than the optical flow-based method, especially when breathing rates are less than 50 bpm. The absolute values for the overall mean errors reduce from 4.8 to 2.7 bpm in the optical flow and deep flow cases, respectively. There is a residual error made by our automated processing pipeline, where the automated method underestimates the respiration rate. This occurs because our band filter fails to remove motion caused by interruptions in the video, for example movement resulting from nurse care-handling. In the future, we will investigate a supervised approach to replace unsupervised filters to extract the noise from respiration signals. We compared the effect of two different flow estimation methods on the final respiration calculation. The results show that the deep flow approach is both sensitive to homogeneous regions and the boundary area, whereas the conventional approach is more sensitive to the boundary area. This advantage of deep flow-based approach improves the whole processing pipeline by increasing both the accuracy and robustness. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Patient ID**} & \multirow{2}{*}{**Duration (h:ms)**} & \multicolumn{3}{c}{**Mean \(\pm\) std**} \\ \cline{3-5}  & & **Reference (CI)** & **Optical Flow** & **Deep Flow** \\ \hline
1 & 00:50:13 & 51.12 \(\pm\) 6.00 & 47.13 \(\pm\) 4.96 & 48.67 \(\pm\) 4.69 \\ \hline
2 & 00:22:00 & 55.41 \(\pm\) 6.73 & 51.18 \(\pm\) 7.35 & 53.23 \(\pm\) 7.07 \\ \hline
3 & 00:26:54 & 48.78 \(\pm\) 3.58 & 44.12 \(\pm\) 2.91 & 46.75 \(\pm\) 2.80 \\ \hline
4 & 00:09:22 & 52.36 \(\pm\) 5.25 & 48.75 \(\pm\) 3.17 & 50.05 \(\pm\) 3.72 \\ \hline
5 & 00:06:15 & 61.61 \(\pm\) 3.80 & 51.56 \(\pm\) 4.29 & 55.58 \(\pm\) 3.28 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Duration of each video, and measured mean and standard deviations of reference and our optical flow- and deep flow-based methods.