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Cited by 64Obstructive Sleep Apnea ResearchNon-Invasive Vital Sign MonitoringSleep and related disorders

Sleep apnea: a review of diagnostic sensors, algorithms, and therapies

Open access
Shokoueinejad, Mehdi, Fernandez, Chris, Carroll, Emily, Wang, Fa, Levin, Jake, Rusk, Sam, Glattard, Nick, Mulchrone, Ashley +5 more·2017·Physiological Measurement
DOI 10.1088/1361-6579/aa6ec6
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Abstract

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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  • PPG accuracy issues potentially affect health-related outcomes that rely on PPG, including sleep duration, sleep architecture, diagnosis of sleep apnea, long-term sleep patterns, and other sleep disorders.

    “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]

  • RR and amplitude of respiration can be extracted from PPG.

    “Therefore, we can extract the RR and amplitude of respiration [98,99,100].”

    From The Current State of Optical Sensors in Medical Wearables (2022) · cites this paper as [100]

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  1. Al-Ashmouny K M, Morsy A A and Loza S F 2006 Sleep apnea detection and classification using fuzzy logic: clinical evaluation 27th Annual Int. Conf. of the Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005 (IEEE) pp 6132-5
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  5. Fernandez C, Rusk S, Glattard N and Shokoueinejad M 2016 Sleep scoring automation via large scale machine learning 38th Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society (IEEE) p 119
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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
DOI
10.1088/1361-6579/aa6ec6
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
Licence
Locations
  • Physiological Measurement (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Medicine › Physiology › 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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