ArticleJournal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine2020
Screening of obstructive sleep apnea in patients who snore using a patch-type device with electrocardiogram and 3-axis accelerometer.
Article in Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 35 citations in OpenAlex.
- A Wearable-Derived Autonomic Component Is Associated with Cardiovascular Health in Community-Dwelling Adults: A Cross-Sectional Analysis from the Longevity Check-Up 8+ Study.Medical sciences (Basel, Switzerland) · 2026Article
- Patch-Type Heart Rate Variability Analysis with Artificial Intelligence for Detection of Obstructive Sleep Apnea.Nature and science of sleep · 2026Article
- Apnea-hypopnea index estimation using overnight chest-wall accelerometry.Frontiers in sleep · 2026Article
- Comparative Treatment Strategies for Obstructive Sleep Apnea and Their Relationships with Aberrant Driving Behaviors: Taxi Drivers in Taipei Metropolitan Area.Nature and science of sleep · 2026Article
- An Automated Algorithm for Obstructive Sleep Apnea Detection Using a Wireless Abdomen-Worn Sensor.Sensors (Basel, Switzerland) · 2025Article
- Utilizing a Wireless Radar Framework in Combination With Deep Learning Approaches to Evaluate Obstructive Sleep Apnea Severity in Home-Setting Environments.Journal of multidisciplinary healthcare · 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
- Combining a wireless radar sleep monitoring device with deep machine learning techniques to assess obstructive sleep apnea severity.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2024Article
- Advances and Opportunities of Mobile Health in the Postpandemic Era: Smartphonization of Wearable Devices and Wearable Deviceization of Smartphones.JMIR mHealth and uHealth · 2024Article
- Comparison of OPPO Watch Sleep Analyzer and Polysomnography for Obstructive Sleep Apnea Screening.Nature and science of sleep · 2024Article
- Convolutional neural network based on photoplethysmography signals for sleep apnea syndrome detection.Frontiers in neuroscience · 2023Article
- Article
- Night-to-night variability of sleep apnea detected by cyclic variation of heart rate during long-term continuous ECG monitoring.Annals of noninvasive electrocardiology : the official journal of the International Society for Holter and Noninvasive Electrocardiology, Inc · 2022Article
- At-home wireless monitoring of acute hemodynamic disturbances to detect sleep apnea and sleep stages via a soft sternal patch.Science advances · 2021Article
- Comparison of Hospital-Based and Home-Based Obstructive Sleep Apnoea Severity Measurements with a Single-Lead Electrocardiogram Patch.Sensors (Basel, Switzerland) · 2021Article
- Assessment of autonomic function by long-term heart rate variability: beyond the classical framework of LF and HF measurements.Journal of physiological anthropology · 2021Review
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
STUDY
objectivesPeople with obstructive sleep apnea (OSA) remain undiagnosed because of the lack of easy and comfortable screening tools. Through this study, we aimed to compare the diagnostic accuracy of chest wall motion and cyclic variation of heart rate (CVHR) in detecting OSA by using a single-lead electrocardiogram (ECG) patch with a 3-axis accelerometer.
methodsIn total, 119 patients who snore simultaneously underwent polysomnography with a single-lead ECG patch. Signals of chest wall motion and CVHR from the single-lead ECG patch were collected. The chest effort index (CEI) was calculated using the chest wall motion recorded by a 3-axis accelerometer in the device. The ability of CEI and CVHR indices in diagnosing moderate-to-severe OSA (apnea-hypopnea index ≥ 15) was compared using the area under the curve (AUC) by using the DeLong test.
resultsCVHR detected moderate-to-severe OSA with 52.9% sensitivity and 94.1% specificity (AUC: 0.76, 95% confidence interval: 0.67-0.84, optimal cutoff: 21.2 events/h). By contrast, CEI identified moderate-to-severe OSA with 80% sensitivity and 79.4% specificity (AUC: 0.87, 95% confidence interval: 0.80-0.94, optimal cutoff: 7.1 events/h). CEI significantly outperformed CVHR regarding the discrimination ability for moderate-to-severe OSA (ΔAUC: 0.11, 95% confidence interval: 0.009-0.21, P = .032). For determining severe OSA, the performance of discrimination ability was greater (AUC = 0.90, 95% confidence interval: 0.85-0.95) when combining these two signals.
conclusionsBoth CEI and CVHR recorded from a patch-type device with ECG and a 3-axis accelerometer can be used to detect moderate-to-severe OSA. Thus, incorporation of CEI is helpful in the detection of sleep apnea by using a single-lead ECG with a 3-axis accelerometer.
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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.