• 主题
高级搜索  >
历史搜索 清空历史
首页> 美国卫生研究院文献>other >Opportunities for Utilizing Polysomnography Signals to Personalize Obstructive Sleep Apnea Subtypes and Severity
【2h】

Opportunities for Utilizing Polysomnography Signals to Personalize Obstructive Sleep Apnea Subtypes and Severity

机译：利用多导睡眠图信号个性化阻塞性睡眠呼吸暂停亚型和严重程度的机会

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务，本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文，但由于OA文献来源多样且变更频繁，仍可能出现获取不到、文献不完整或与标题不符等情况，如果获取不到我们将提供退款服务。请知悉。
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Obstructive Sleep Apnea (OSA) is a heterogeneous sleep disorder with many pathophysiological pathways to disease. Currently, the diagnosis and classification of OSA is based on the apnea-hypopnea index, which poorly correlates to underlying pathology and clinical consequences. A large number of in-laboratory sleep studies are performed around the world every year, already collecting an enormous amount of physiological data within an individual. Clinically, we have not yet fully taken advantage of this data, but combined with existing analytical approaches, we have the potential to transform the way OSA is managed within an individual patient. Currently, respiratory signals are used to count apneas and hypopneas, but patterns such as inspiratory flow signals can be used to predict optimal OSA treatment. Electrocardiographic data can reveal arrhythmias, but patterns such as heart rate variability can also be used to detect and classify OSA. Electroencephalography is used to score sleep stages and arousals, but specific patterns such as the odds-ratio product can be used to classify how OSA patients responds differently to arousals. In this review, we examine these and many other existing computer-aided polysomnography signal processing algorithms and how they can reflect an individual’s manifestation of OSA. Together with current technological advance, it is only a matter of time before advanced automatic signal processing and analysis is widely applied to precision medicine of OSA in the clinical setting.
机译：阻塞性睡眠呼吸暂停（OSA）是一种异质性睡眠障碍，具有多种疾病的病理生理途径。当前，OSA的诊断和分类基于呼吸暂停-呼吸不足指数，与基础病理学和临床后果之间的相关性很差。全世界每年都进行大量的实验室内睡眠研究，已经在一个人体内收集了大量的生理数据。临床上，我们尚未充分利用此数据，但结合现有的分析方法，我们有可能改变在单个患者中管理OSA的方式。当前，呼吸信号被用于计算呼吸暂停和呼吸不足，但是诸如吸气流量信号的模式可以被用来预测最佳的OSA治疗。心电图数据可显示心律不齐，但心率变异性等模式也可用于检测和分类OSA。脑电图用于对睡眠阶段和唤醒进行评分，但是可以使用诸如赔率比乘积之类的特定模式来分类OSA患者对唤醒的不同反应。在这篇评论中，我们研究了这些以及许多其他现有的计算机辅助多导睡眠图信号处理算法，以及它们如何反映个人对OSA的表现。结合当前的技术进步，先进的自动信号处理和分析在临床环境中被广泛应用于OSA精密医学只是时间问题。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号