Sleep apnea: a review of diagnostic sensors, algorithms, and therapies
- 18 August 2017
- journal article
- review article
- Published by IOP Publishing in Physiological Measurement
- Vol. 38 (9), R204-R252
- https://doi.org/10.1088/1361-6579/aa6ec6
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.This publication has 100 references indexed in Scilit:
- Sleep scoring using artificial neural networksSleep Medicine Reviews, 2012
- Particle swarm optimization for feature selection with application in obstructive sleep apnea diagnosisNeural Computing & Applications, 2011
- Review of oral appliances for treatment of sleep-disordered breathingSleep and Breathing, 2006
- A Respiratory Movement Monitoring System Using Fiber-Grating Vision Sensor for Diagnosing Sleep Apnea SyndromeOptical Review, 2005
- Intelligent diagnosis of sleep apnea syndromeIEEE Engineering in Medicine and Biology Magazine, 2004
- The Prevalence, Cost Implications, and Management of Sleep Disorders: An OverviewSleep and Breathing, 2002
- Photorefractive properties of near-stoichiometric LiNbO3 grown from congruent melts containing K2OJournal of Applied Physics, 2001
- Experimental evaluation of two new sensors for respiratory rate monitoringPhysiological Measurement, 1993
- Comparison of the response of diaphragm and upper airway dilating muscle activity in sleeping catsRespiration Physiology, 1987
- REVERSAL OF OBSTRUCTIVE SLEEP APNOEA BY CONTINUOUS POSITIVE AIRWAY PRESSURE APPLIED THROUGH THE NARESThe Lancet, 1981