Tracing the evolution in sleep apnea detection: a review from traditional non-contact under-the-mattress devices to advanced AI-driven methods.
Sleep apnea is traditionally diagnosed with polysomnography (PSG), which, while effective, is costly, time-consuming, and obtrusive. Recent advancements in biosensing technologies have facilitated the development of under-the-mattress devices as potential alternatives for detecting sleep apnea.
We reviewed the literature across PubMed, Embase, Web of Science, and Scopus, focusing on studies that assessed mattress-like or under-the-mattress biosensing devices for sleep apnea. 15 studies were included as illustrative examples of recent progress.
Our review assessed studies on innovative sensor technologies for sleep apnea detection. These studies demonstrated the efficacy of various sensors-such as Load Cells, Emfit, and PVDF-along with advanced radar and machine learning methods, in accurately identifying sleep apnea events. Results indicated that most studies reported good overall performance of mattress-based systems compared to traditional polysomnography, though variability across devices was observed.
Under-the-mattress biosensing devices appear to be promising as cost-effective, user-friendly, and unobtrusive alternatives to PSG for sleep apnea detection. Their high-performance metrics suggest that these devices are viable options for both clinical settings and home use.
We reviewed the literature across PubMed, Embase, Web of Science, and Scopus, focusing on studies that assessed mattress-like or under-the-mattress biosensing devices for sleep apnea. 15 studies were included as illustrative examples of recent progress.
Our review assessed studies on innovative sensor technologies for sleep apnea detection. These studies demonstrated the efficacy of various sensors-such as Load Cells, Emfit, and PVDF-along with advanced radar and machine learning methods, in accurately identifying sleep apnea events. Results indicated that most studies reported good overall performance of mattress-based systems compared to traditional polysomnography, though variability across devices was observed.
Under-the-mattress biosensing devices appear to be promising as cost-effective, user-friendly, and unobtrusive alternatives to PSG for sleep apnea detection. Their high-performance metrics suggest that these devices are viable options for both clinical settings and home use.