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

Mehdi Shokoueinejad1,2,3, Chris Fernandez2,3, Emily Carroll4, Fa Wang5, Jake Levin1, Sam Rusk2,3, Nick Glattard2,3, Ashley Mulchrone1, Xuan Zhang5, Ailiang Xie2, Mihaela Teodorescu6, Jerome Dempsey2 and John Webster1

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.

1 Department of Biomedical Engineering, University of Wisconsin–Madison,
1550 Engineering Drive, Madison, WI 53706-1609, United States of America
2 Department of Population Health Sciences, University of Wisconsin–Madison, 610 Walnut St 707, Madison, WI 53726, United States of America
3 EnsoData Research, EnsoData Inc., 111N Fairchild St, Suite 240, Madison, WI 53703, United States of America
4 Department of Electrical Engineering, University of Minnesota, 200 Union Street SE, Minneapolis, MN 55455, United States of America
5 Department of Electrical and Computer Engineering, University of
Wisconsin–Madison, 1415 Engineering Drive, Madison, WI 53706-1691, United States of America
6 Department of Medicine, University of Wisconsin–Madison, 1685 Highland Ave, Madison, WI 53792, United States of America

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