Non-contact Infant Sleep Apnea Detection
This addresses the need for accurate, non-contact monitoring in infants to prevent long-term health issues, representing an incremental improvement over existing non-contact methods.
The paper tackles the problem of non-contact sleep apnea detection in infants by developing a novel video processing algorithm that is accurate and lightweight enough to run on a single-board computer, achieving unspecified accuracy on real data.
Sleep apnea is a breathing disorder where a person repeatedly stops breathing in sleep. Early detection is crucial for infants because it might bring long term adversities. The existing accurate detection mechanism (pulse oximetry) is a skin contact measurement. The existing non-contact mechanisms (acoustics, video processing) are not accurate enough. This paper presents a novel algorithm for the detection of sleep apnea with video processing. The solution is non-contact, accurate and lightweight enough to run on a single board computer. The paper discusses the accuracy of the algorithm on real data, advantages of the new algorithm, its limitations and suggests future improvements.