de Chazal Philip, Heneghan Conor, McNicholas Walter T
BiancaMed, NovaUCD, University College Dublin, Belfield, Dublin 4, Ireland.
Philos Trans A Math Phys Eng Sci. 2009 Jan 28;367(1887):369-89. doi: 10.1098/rsta.2008.0156.
A method for the detection of sleep apnoea, suitable for use in the home environment, is presented. The method automatically analyses night-time electrocardiogram (ECG) and oximetry recordings and identifies periods of normal and sleep-disordered breathing (SDB). The SDB is classified into one of six classes: obstructive, mixed and central apnoeas, and obstructive, mixed and central hypopnoeas. It also provides an estimated apnoea, hypopnoea and apnoea-hypopnoea index. The basis of the method is a pattern recognition system that identifies episodes of apnoea by analysing the heart variability, an ECG-derived respiration signal and blood oximetry values. The method has been tested on 183 subjects with a range of apnoea severities who have undergone a full overnight polysomnogram study. The results show that the method separates control subjects from subjects with clinically significant sleep apnoea with a specificity of 83 per cent and sensitivity of 95 per cent. These results demonstrate that home-based screening for sleep apnoea is a viable alternative to hospital-based tests with the added benefit of low cost and minimal waiting times.
本文介绍了一种适用于家庭环境的睡眠呼吸暂停检测方法。该方法可自动分析夜间心电图（ECG）和血氧饱和度记录，并识别正常呼吸和睡眠呼吸紊乱（SDB）的时段。SDB被分为六类之一：阻塞性、混合性和中枢性呼吸暂停，以及阻塞性、混合性和中枢性呼吸浅慢。它还提供估计的呼吸暂停、呼吸浅慢和呼吸暂停-呼吸浅慢指数。该方法的基础是一个模式识别系统，该系统通过分析心率变异性、基于ECG的呼吸信号和血氧饱和度值来识别呼吸暂停发作。该方法已在183名患有不同严重程度呼吸暂停的受试者身上进行了测试，这些受试者均接受了完整的夜间多导睡眠图研究。结果表明，该方法将对照受试者与具有临床意义的睡眠呼吸暂停受试者区分开来，特异性为83%，敏感性为95%。这些结果表明，基于家庭的睡眠呼吸暂停筛查是一种可行的替代医院检测的方法，具有低成本和最短等待时间的额外优势。
