Sleep & breathing = Schlaf & Atmung · 2025
Tracing the evolution in sleep apnea detection: a review from traditional non-contact under-the-mattress devices to advanced AI-driven methods.
СтатьяОбзорНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
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История · было → стало
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Daneshvar Sara, Rahimi Mehran, Ansarin Khalil
Аннотация
BACKGROUND: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSION: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSION: 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.