ArticlePloS one2020
Quantitative detection of sleep apnea with wearable watch device.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 2 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
32 citing papers in PubMed, 2 syntheses or guidelines pooled it, 59 citations in OpenAlex.
- Home-testing devices for diagnosing obstructive sleep apnoea hypopnoea syndrome - a systematic review and economic evaluation.Health technology assessment (Winchester, England) · 2026Pooled it
- The Role of Novel Digital Clinical Tools in the Screening or Diagnosis of Obstructive Sleep Apnea: Systematic Review.Journal of medical Internet research · 2023Pooled it
- Screening for Sleep-Disordered Breathing Risk in Pediatric Dental Care: A Protocol Combining Questionnaire-Based Stratification and Wearable Home Sleep Monitoring.Methods and protocols · 2026Article
- Towards automatic home screening of obstructive sleep apnea using combined features from pulse wave amplitude, pulse-to-pulse interval and oxygen desaturation.Sleep & breathing = Schlaf & Atmung · 2026Article
- Distributed Precision Stroke Care: Artificial Intelligence-Driven Stroke Management Using Multimodal Sensor Data.Stroke · 2026Review
- Multicenter Validation of an AI-Based Snoring Model vs. Wearables for Diagnosing Sleep Apnea.Nature and science of sleep · 2026Article
- Article
- Artificial intelligence identifies individuals with prediabetes using single-lead electrocardiograms.Cardiovascular diabetology · 2025Article
- Detection of sleep apnea using smartphone-embedded inertial measurement unit.Scientific reports · 2025Article
- Clinical-level screening of sleep apnea syndrome with single-lead ECG alone is achievable using machine learning with appropriate time windows.Sleep & breathing = Schlaf & Atmung · 2025Article
- Innovative Micro- and Nano-Architectures in Biomedical Engineering for Therapeutic and Diagnostic Applications.Micromachines · 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
- Wearable sensors-based assistive technologies for patient health monitoring.Frontiers in bioengineering and biotechnology · 2025Article
- AI‑Enhanced Smartwatch AHI Estimation and AI‑Scored Polysomnography for Obstructive Sleep Apnea: Real‑World Validation.Nature and science of sleep · 2025Article
- Study on OSA screening and influencing factors in community-based elderly hypertensive patients based on single-lead wearable ECG devices.Sleep & breathing = Schlaf & Atmung · 2024Article
- Artificial Intelligence, the Production of Scientific Texts, and the Implications for Sleep Science: Exploring Emerging Paradigms and Perspectives.Sleep science (Sao Paulo, Brazil) · 2024Article
- Article
- Piezoelectric rubber sheet sensor: a promising tool for home sleep apnea testing.Sleep & breathing = Schlaf & Atmung · 2024Article
- Quantitative detection of sleep apnea in adults using inertial measurement unit embedded in wristwatch wearable devices.Scientific reports · 2024Article
- Efficient Feature-Selection-Based Stacking Model for Stress Detection Based on Chest Electrodermal Activity.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The spread of wearable watch devices with photoplethysmography (PPG) sensors has made it possible to use continuous pulse wave data during daily life. We examined if PPG pulse wave data can be used to detect sleep apnea, a common but underdiagnosed health problem associated with impaired quality of life and increased cardiovascular risk. In 41 patients undergoing diagnostic polysomnography (PSG) for sleep apnea, PPG was recorded simultaneously with a wearable watch device. The pulse interval data were analyzed by an automated algorithm called auto-correlated wave detection with adaptive threshold (ACAT) which was developed for electrocardiogram (ECG) to detect the cyclic variation of heart rate (CVHR), a characteristic heart rate pattern accompanying sleep apnea episodes. The median (IQR) apnea-hypopnea index (AHI) was 17.2 (4.4-28.4) and 22 (54%) subjects had AHI ≥15. The hourly frequency of CVHR (Fcv) detected by the ACAT algorithm closely correlated with AHI (r = 0.81), while none of the time-domain, frequency-domain, or non-linear indices of pulse interval variability showed significant correlation. The Fcv was greater in subjects with AHI ≥15 (19.6 ± 12.3 /h) than in those with AHI <15 (6.4 ± 4.6 /h), and was able to discriminate them with 82% sensitivity, 89% specificity, and 85% accuracy. The classification performance was comparable to that obtained when the ACAT algorithm was applied to ECG R-R intervals during the PSG. The analysis of wearable watch PPG by the ACAT algorithm could be used for the quantitative screening of sleep apnea.
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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.