Shokoueinejad Mehdi, Fernandez Chris, Carroll Emily, Wang Fa, Levin Jake, Rusk Sam, Glattard Nick, Mulchrone Ashley, Zhang Xuan, Xie Ailiang, Teodorescu Mihaela, Dempsey Jerome, Webster John
Department of Biomedical Engineering, University of Wisconsin-Madison, 1550 Engineering Drive, Madison, WI 53706-1609, United States of America. Department of Population Health Sciences, University of Wisconsin-Madison, 610 Walnut St 707, Madison, WI 53726, United States of America. EnsoData Research, EnsoData Inc., 111 N Fairchild St, Suite 240, Madison, WI 53703, United States of America.
Physiol Meas. 2017 Aug 18;38(9):R204-R252. doi: 10.1088/1361-6579/aa6ec6.
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.
This article reviews the current engineering approaches for the detection and treatment of sleep apnea.
It discusses signal acquisition and processing, highlights the current nonsurgical and nonpharmacological treatments, and discusses potential new therapeutic approaches.
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.
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.
尽管公众对睡眠相关疾病的认识在不断提高，但睡眠呼吸暂停综合征（SAS）仍然是一个公共卫生和经济挑战。在过去二十年中，广泛的对照流行病学研究已经阐明了发病率、包括肥胖流行在内的危险因素以及阻塞性睡眠呼吸暂停（OSA）的全球患病率，同时也建立了越来越多的文献，将OSA与心血管疾病、死亡率、代谢失调和神经认知障碍联系起来。美国医学研究所睡眠医学委员会估计，美国有5000万至7000万成年人存在睡眠或觉醒障碍。此外，美国睡眠医学学会（AASM）估计，超过2900万美国成年人患有中度至重度OSA，估计其中80%的人未意识到且未被诊断，这在2015年导致了超过1496亿美元的医疗保健和其他费用。尽管已经使用了各种设备来测量生理信号、检测呼吸暂停事件并帮助治疗睡眠呼吸暂停，但在提高睡眠呼吸暂停护理的质量、效率和可承受性方面仍有很大机会。随着我们对呼吸和神经生理信号以及睡眠呼吸暂停生理机制的理解不断加深，以及我们检测和处理生物医学信号的能力不断提高，新的诊断和治疗方式不断涌现。
本文综述了当前用于检测和治疗睡眠呼吸暂停的工程方法。
讨论信号采集和处理，强调当前的非手术和非药物治疗，并讨论潜在的新治疗方法。
这项工作带来了一系列经过验证的信号和传感器模式，用于获取、存储和查看睡眠数据；广泛的计算和信号处理方法，用于检测和分类SAS疾病模式；以及一套独特的治疗技术，其用例涵盖了疾病严重程度的连续体。
本综述提供了当前可用工具类别的视角，以及它们的相对优势和进一步改进的领域。
