ArticleScientific reports2024
Quantitative detection of sleep apnea in adults using inertial measurement unit embedded in wristwatch wearable devices.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 12 citations in OpenAlex.
- Article
- Physical activity and cancer biology: a narrative review of molecular mechanisms and introduction of the SCRUM-MONSTAR LIFELOG study.International journal of clinical oncology · 2025Review
- Detection of sleep apnea using smartphone-embedded inertial measurement unit.Scientific reports · 2025Article
- Diagnostic Modalities in Sleep Disordered Breathing: Current and Emerging Technology and Its Potential to Transform Diagnostics.Respirology (Carlton, Vic.) · 2025Review
- Transforming Sleep Monitoring: Review of Wearable and Remote Devices Advancing Home Polysomnography and Their Role in Predicting Neurological Disorders.Biosensors · 2025Review
- Detection of sleep apnea using only inertial measurement unit signals from apple watch: a pilot-study with machine learning approach.Sleep & breathing = Schlaf & Atmung · 2025Article
- Logical reasoning for human activity recognition based on multisource data from wearable device.Scientific reports · 2025Article
- AI‑Enhanced Smartwatch AHI Estimation and AI‑Scored Polysomnography for Obstructive Sleep Apnea: Real‑World Validation.Nature and science of sleep · 2025Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sleep apnea (SA) is associated with risk of cardiovascular disease, cognitive decline, and accidents due to sleepiness, yet the majority (over 80%) of patients remain undiagnosed. Inertial measurement units (IMUs) are built into modern wearable devices and are capable of long-term continuous measurement with low power consumption. We examined if SA can be detected by an IMU embedded in a wristwatch device. In 122 adults who underwent polysomnography (PSG) examinations, triaxial acceleration and triaxial gyro signals from the IMU were recorded during the PSG. Subjects were divided into a training group and a test groups (both n = 61). In the training group, an algorithm was developed to extract signals in the respiratory frequency band (0.13-0.70 Hz) and detect respiratory events as transient (10-90 s) decreases in amplitude. The respiratory event frequency estimated by the algorithm correlated with the apnea-hypopnea index (AHI) of the PSG with r = 0.84 in the test group. With the cutoff values determined in the training group, moderate-to-severe SA (AHI ≥ 15) was identified with 85% accuracy and severe SA (AHI ≥ 30) with 89% accuracy in the test group. SA can be quantitatively detected by the IMU embedded in wristwatch wearable devices in adults with suspected SA.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.