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Sleep apnea: a review of diagnostic sensors, algorithms, and therapies
Open accessAbstract
While public awareness of sleep related disorders is growing, sleep apnea syndrome (SAS) remains a public health and economic challenge. Over the last two decades, extensive controlled epidemiologic research has clarified the incidence, risk factors including the obesity epidemic, and global prevalence of obstructive sleep apnea (OSA), as well as establishing a growing body of literature linking OSA with cardiovascular morbidity, mortality, metabolic dysregulation, and neurocognitive impairment. The US Institute of Medicine Committee on Sleep Medicine estimates that 50-70 million US adults have sleep or wakefulness disorders. Furthermore, the American Academy of Sleep Medicine (AASM) estimates that more than 29 million US adults suffer from moderate to severe OSA, with an estimated 80% of those individuals living unaware and undiagnosed, contributing to more than $149.6 billion in healthcare and other costs in 2015. Although various devices have been used to measure physiological signals, detect apneic events, and help treat sleep apnea, significant opportunities remain to improve the quality, efficiency, and affordability of sleep apnea care. As our understanding of respiratory and neurophysiological signals and sleep apnea physiological mechanisms continues to grow, and our ability to detect and process biomedical signals improves, novel diagnostic and treatment modalities emerge. OBJECTIVE: This article reviews the current engineering approaches for the detection and treatment of sleep apnea. APPROACH: It discusses signal acquisition and processing, highlights the current nonsurgical and nonpharmacological treatments, and discusses potential new therapeutic approaches. MAIN RESULTS: This work has led to an array of validated signal and sensor modalities for acquiring, storing and viewing sleep data; a broad class of computational and signal processing approaches to detect and classify SAS disease patterns; and a set of distinctive therapeutic technologies whose use cases span the continuum of disease severity. SIGNIFICANCE: This review provides a current perspective of the classes of tools at hand, along with a sense of their relative strengths and areas for further improvement.
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“This potentially affects health-related outcomes that rely on PPG including a host of sleep-related indices such as sleep duration, architecture [8], diagnosis of sleep apnea [9], long-term sleep patterns, and other sleep disorders [10].”
From Limiting racial disparities and bias for wearable devices in health science research (2020) · cites this paper as [9]
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“Therefore, we can extract the RR and amplitude of respiration [98,99,100].”
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Machine Learning Models Using Electronic Health Record Data to Predict Obstructive Sleep ApneaPreprint
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Metadata
- Authors
- Shokoueinejad, M., Fernandez, C., Carroll, E., Wang, F., Levin, J., Rusk, S., Glattard, N., Mulchrone, A., Zhang, X., Xie, A., Teodorescu, M., Dempsey, J., & Webster, J.
- Affiliations
- University of Wisconsin–Madison (US)
- University of Minnesota (US)
- Corresponding author
- Shokoueinejad, Mehdi
- Webster, John
- ORCID
- Venue
- Physiological Measurement
- Publisher
- IOP Publishing
- Published
- 2017-08-18
- Volume
- 38
- Issue
- 9
- Pages
- R204-R252
- ISSN
- 0967-3334 (ISSN-L)
- 1361-6579
- OpenAlex
- W2750410834
- Cited by
- 64
- References
- 233
- Citations by year
- 2026: 7 · 2025: 2 · 2024: 6 · 2023: 3 · 2022: 10 · 2021: 4 · 2020: 11 · 2019: 9 · 2018: 9 · 2017: 3
- FWCI
- 1.07
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Physiological Measurement (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Physiology (medical) › Obstructive Sleep Apnea Research
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Sleep and related disorders
- Keywords
- obstructive sleep apnea, sleep apnea, sleep apnea diagnosis, sleep apnea treatment, physiological signal acquisition, biomedical sensors, signal processing, non-pharmacological treatment, cardiovascular risk, obesity, health care costs, undiagnosed sleep apnea
- MeSH terms
- Algorithms, Humans, Signal Processing, Computer-Assisted, Sleep Apnea Syndromes, Diagnostic Equipment
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