Department of Medical Education, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan.
Circ Arrhythm Electrophysiol. 2011 Feb;4(1):64-72. doi: 10.1161/CIRCEP.110.958009. Epub 2010 Nov 12.
Despite the adverse cardiovascular consequences of obstructive sleep apnea, the majority of patients remain undiagnosed. To explore an efficient ECG-based screening tool for obstructive sleep apnea, we examined the usefulness of automated detection of cyclic variation of heart rate (CVHR) in a large-scale controlled clinical setting.
We developed an algorithm of autocorrelated wave detection with adaptive threshold (ACAT). The algorithm was optimized with 63 sleep studies in a training cohort, and its performance was confirmed with 70 sleep studies of the Physionet Apnea-ECG database. We then applied the algorithm to ECGs extracted from all-night polysomnograms in 862 consecutive subjects referred for diagnostic sleep study. The number of CVHR per hour (the CVHR index) closely correlated (r=0.84) with the apnea-hypopnea index, although the absolute agreement with the apnea-hypopnea index was modest (the upper and lower limits of agreement, 21 per hour and -19 per hour) with periodic leg movement causing most of the disagreement (P<0.001). The CVHR index showed a good performance in identifying the patients with an apnea-hypopnea index ≥15 per hour (area under the receiver-operating characteristic curve, 0.913; 83% sensitivity and 88% specificity, with the predetermined cutoff threshold of CVHR index ≥15 per hour). The classification performance was unaffected by older age (≥65 years) or cardiac autonomic dysfunction (SD of normal-to-normal R-R intervals over the entire length of recording <65 ms; area under the receiver-operating characteristic curve, 0.915 and 0.911, respectively).
The automated detection of CVHR with the ACAT algorithm provides a powerful ECG-based screening tool for moderate-to-severe obstructive sleep apnea, even in older subjects and in those with cardiac autonomic dysfunction.
尽管阻塞性睡眠呼吸暂停对心血管有不良影响，但大多数患者仍未被诊断出来。为了探索一种有效的基于心电图的阻塞性睡眠呼吸暂停筛查工具，我们在大规模对照临床环境中检查了自动检测心率周期性变化（CVHR）的有用性。
我们开发了一种具有自适应阈值的自相关波检测算法（ACAT）。该算法在训练队列中的 63 项睡眠研究中进行了优化，并在 Physionet Apnea-ECG 数据库的 70 项睡眠研究中得到了验证。然后，我们将该算法应用于 862 例连续进行诊断性睡眠研究的患者的整夜多导睡眠图中提取的心电图。每小时的 CVHR 数（CVHR 指数）与呼吸暂停低通气指数密切相关（r=0.84），尽管与呼吸暂停低通气指数的绝对一致性不太理想（一致性上下限为每小时 21 次和每小时-19 次），周期性肢体运动导致大部分不一致（P<0.001）。CVHR 指数在识别呼吸暂停低通气指数≥15 次/小时的患者方面表现出良好的性能（受试者工作特征曲线下面积为 0.913；83%的敏感性和 88%的特异性，预定的 CVHR 指数≥15 次/小时的截断阈值）。分类性能不受年龄较大（≥65 岁）或心脏自主神经功能障碍（整个记录长度内正常到正常 R-R 间隔的标准差<65 毫秒）的影响（受试者工作特征曲线下面积分别为 0.915 和 0.911）。
ACAT 算法自动检测 CVHR 为中重度阻塞性睡眠呼吸暂停提供了一种强大的基于心电图的筛查工具，即使在老年患者和心脏自主神经功能障碍患者中也是如此。
