# Respiration Monitoring for Premature Neonates in NICU
## 2 Related Work  
In the applications of adults, [PERSON] et al. [10] presented a design and implementation of a novel sleep monitoring system that simultaneously analyzed respiration, head posture, and body posture for adults. For respiration, the region of breathing movement was automatically determined and the intensity estimated, yielding a waveform indicating respiratory rhythms with an accuracy of 96% in recognizing abnormal breathing. However, the accuracy of the respiration rate was not provided. [PERSON] et al. [11] applied a contactless biomotion sensor of Doppler radar for respiration monitoring. Respiratory frequency and magnitude were used for the classification of determining sleep/wake states, which achieved the accuracy of 69% for the wake and 88% for sleep state. [PERSON] et al. [12] estimated heart rate (HR) accurately, using face videos acquired from a low-cost camera for adults. The face video consisting of frontal, profile, or multiple faces, was divided into multiple overlapping fragments to determine HR estimates. The HR estimates were fused using quality-based fusion, which aimed to minimize illumination and face deformations. [PERSON] et al. [13] proposed a general framework for estimating a periodic signal, which was applied to derive a computationally inexpensive method for estimating respiratory pattern using two-dimensional cameras that did not critically depend on the region of interest. Specifically, the patterns were estimated by imaging changes in the reflected light caused by respiration-induced motion. Estimation of the pattern was cast as a blind deconvolution problem and was solved through a method comprising subspace projection and statistical aggregation. In terms of the applications for infants, [PERSON] et al. reviewed the unobtrusive measurements for indicating sleep state in preterm infants [14]. [PERSON] et al. [15] attempted to detect respiration rate of neonates on a real-time basis using infrared thermography. They analyzed the anterior naris (nostrils) temperature profile associated with the inspiration and expiration phases. However, the region of interest (ROI) was assumed to be fixed after initialization. Moreover, the method is not practical for NICU infants, since the faces of premature infants in a NICU are often occluded by feeding tubes and/or breathing masks. In practice, the temperature inside an incubator is continuously monitored and adjusted by caregivers in order to help infants maintain their body temperature in a normal range. However, the controlled environmental temperature might affect the accuracy of the thermography-based respiration monitoring method. [PERSON] et al. [16] extracted the respiration rate from video data included in polysomnography. They used Eulerian video magnification (EVM) to amplify the respiration movements, which was followed by optical flow to estimate the respiration motion and therefore, obtained a respiration signal. Independent component analysis and principal component analysis were applied to improve signal quality. Finally, the results showed a detection accuracy of 94.12% for sleeping-stage patients. [PERSON] et al. [17] applied a digital webcam (WeC) and an EVM algorithm to measure HR and respiration rate (RR). The accumulated RGB values of a manually selected ROI were calculated as a single signal, from which the power spectral density was further estimated and used for peak extraction. The evaluation based on data of seven patients yielded a root mean-squared error (RMSE) of 12.2 for the HR and 7.6 for the RR. However, the limitation of a pulse-based respiration extraction is that it extracts the respiratory modulation in blood volume changes, which is both subject dependent (different respiratory efforts) and measurement-locationdependent. The modulation effect varies in different body parts. Moreover, the peak selection from spectrograms was based on common knowledge of normal HR and RR frequency ranges, which is not suitable for infants under clinical conditions of different diseases.