ArticleNature and science of sleep2023
Measuring Sleep Stages and Screening for Obstructive Sleep Apnea with a Wearable Multi-Sensor System in Comparison to Polysomnography.
Article in Nature and science of sleep, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed, 8 citations in OpenAlex.
- Sleep stage classification from ECG using machine learning: Evaluating the impact of signal duration.Neurobiology of sleep and circadian rhythms · 2026Article
- Article
- Artificial intelligence for sleep-disordered breathing: a taxonomy of methods, data modalities, and clinical applications.Frontiers in sleep · 2026Review
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Authors and funding
12 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: To assess the performance of a wearable multi-sensor system (SensEcho) in comparison to polysomnography (PSG) in measuring sleep stages and searching for obstructive sleep apnea (OSA). Methods: Participants underwent overnight simultaneous monitoring using SensEcho and PSG in a sleep laboratory. SensEcho analyzed the recordings spontaneously, and PSG was assessed as per standard guidelines. The degree of snoring was evaluated according to the guidelines for the diagnosis and treatment of OSA hypopnea syndrome (2011 revision). The Epworth Sleepiness Scale (ESS) was used to assess general daytime sleepiness. Results: This study included 103 Han Chinese, 91 of whom (age 39.02 ± 13.84 years, body mass index 27.28 ± 5.12 kg/m2, 61.54% male) completed the assessments. The measures of total sleep time (P = 0.198); total wake time (P = 0.182); shallow sleep (P = 0.297), deep sleep (P = 0.422), rapid eye movement sleep (P = 0.570), and awake (P = 0.336) proportions were similar between SensEcho and PSG. Using an apnea-hypopnea index (AHI) cutoff of ≥ 5 events/h, the SensEcho had 82.69% sensitivity and 89.74% specificity. Almost the same results were obtained at an AHI threshold of ≥ 15 events/h. Although the specificity increased to 94.67%, it decreased to 43.75% at an AHI cutoff of ≥ 30 events/h. Conclusion: This study demonstrated that SensEcho can be used to evaluate sleep status and screen for OSA. Nevertheless, improving the accuracy of its assessment of severe OSA and further testing its effectiveness in community and home environments is necessary.
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