# Respiration Monitoring for Premature Neonates in NICU
## 4 Experimental Results and Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Patient ID**} & \multicolumn{2}{c}{**RMSE**} & \multicolumn{2}{c}{**CC Coefficients**} \\ \cline{2-5}  & **Optical Flow** & **Deep Flow** & **Optical Flow** & **Deep Flow** \\ \hline
1 & 5.09 & 4.32 & 0.85 & 0.82 \\ \hline
2 & 4.94 & 3.10 & 0.94 & 0.95 \\ \hline
3 & 3.86 & 3.51 & 0.73 & 0.75 \\ \hline
4 & 5.75 & 5.11 & 0.47 & 0.52 \\ \hline
5 & 10.85 & 6.71 & 0.49 & 0.66 \\ \hline Average & 6.10 & 4.55 & 0.70 & 0.74 \\We consider that the accuracy of our algorithm for extracting respiration signal may be affected by the captured image resolution and image sensor noise. If the resolution is rather low, the movement information from different regions can be blended within one pixel, which is not ideal for accurate motion extraction. If the image sensor noise is too high such that the sensor noise components in pixel values dominate the pixel changes induced by respiratory motion, the measurement will be polluted. Figure 6: Correlation and Bland–Altman plots comparing respiration rate measurements derived from: (**a**) the CI and optical flow-based method; and (**b**) the CI and deep flow-based method. The correlation plots contain the linear regression equation, correlation coefficient (\(r^{2}\)), sum of squared error (SSE), and number of points. The Bland–Altman plots contain the reproducibility coefficient (RPC = \(1.96*\sigma\)), and also show the coefficient of variation (CV = the standard deviation (\(\sigma\)) as a percentage of the mean), limits of agreement (LOA = \(\pm 1.96*\sigma\)), and the bias offset of the measures. The quantitative analysis regarding this perspective is a complicated matter and will be performed as future work. Currently, the work is carried out as a feasibility study. The focus of this study is the installation, adaptation, and validation of camera-based monitoring technology in the NICU setting. We have not investigated the performance on preterm infants related to specific diseases. In the future, we will further validate our algorithm on infants having different health situations. Our recordings were taken from real clinical practice without interfering with the clinical workflow. Our algorithm works when the respiratory motion can be observed by the camera, even as subtle movement, i.e., infants can be either naked or covered by a blanket (as long as the infant body has contact with the blanket such that the movement information from the thorax-abdomen can still be derived). Our algorithm relies on the intensity of video frames for motion extraction (i.e., no color or chromaticity information is used). Therefore, for the nighttime condition, it is possible to measure the respiration signal by using the same software algorithms that we have proposed and just altering to an infrared camera with an infrared lighting source. During low-light conditions, the performance of our system may be affected by the noise induced by the camera sensor. The highlight of using a video-based method to monitor respiration is its contact-free operation. Both CI and polysomnography need electrodes attached to the patient's skin, which increase the risk of skin irritation. Therefore, our method can improve the comfort level and convenience. A further benefit of using a camera is that it enables more measurements than contact-based bio-sensors, including physiological signals (e.g., breathing rate, heart rate, and blood oxygen saturation) [24; 25] and contexture signals (e.g., body motion, activities, and facial expressions) [26; 27; 28; 29]. This will enrich the functionality of a health monitoring system. Our system can be constructed with a generic webcam and an embedded computing platform, which forms a cost-effective solution. In principle, one camera can monitor multiple subjects/infants simultaneously, as long as they are captured by the camera view, while each contact-based bio-sensor can only monitor one single subject/infant.