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首页> 外文期刊>Journal of Electrocardiology: An International Publication for the Study of the Electrical Activities of the Heart >Electrocardiogram-derived respiration in screening of sleep-disordered breathing
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Electrocardiogram-derived respiration in screening of sleep-disordered breathing

机译：心电图派生呼吸法在睡眠呼吸障碍筛查中的应用

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Methods for assessment of sleep-disordered breathing (SDB), including sleep apnea, range from a simple questionnaire to complex multichannel polysomnography. Inexpensive and efficient electrocardiogram (ECG)-based solutions could potentially fill the gap and provide a new SDB screening tool. In addition to the heart rate variability (HRV)-based SDB screening method that we reported a year ago, we have developed a novel method based on ECG-derived respiration (EDR). This method derives the respiratory waveform by (a) measuring peak-to-trough QRS amplitude in a single-channel ECG, (b) removing outlier introduced by noise and artifacts, (c) interpolating the derived values, and (d) filtering values within the respiration rates of 5 and 25 cycles per minute. Each 30 seconds of the respiratory waveform is then classified as normal, SDB, or indeterminate epoch. The previously reported HRV-based method, applied at the same time, is based on power spectrum of heart rate over a sliding 6-minute time window to classify the middle 30-second epoch. We then combined the EDR- and HRV-based techniques to optimize the classification of each epoch. The combined method further improved the accuracy of SDB screening in an independent test database with annotated SDB epochs. The development database was from PhysioNet (n = 25 polysomnograms). The test database was from Sleep Health Centers in Boston (n = 1907 polysomnogram) where the SDB epochs (n = 1 538 222 epochs) were scored using American Academy of Sleep Medicine criteria. The first test was to classify every epoch in the evaluation data set. The combined EDR and HRV method classified 78% of the epochs as either normal or SDB and 22% as indeterminate, with a total accuracy of 88% for scored epochs (not indeterminate). The second test was to evaluate the SDB status for each patient. The algorithm correctly classified 71% of patients with either moderate-to-severe SDB or mild-to-no SDB. We believe that the ECG-based methods provide an efficient and inexpensive tool for SDB screening in both home and hospital settings and make SDB screening feasible in large populations.
机译：包括睡眠呼吸暂停在内的睡眠呼吸障碍（SDB）评估方法从简单的问卷调查表到复杂的多通道多导睡眠图。基于廉价且高效的心电图（ECG）的解决方案可能会填补空白，并提供新的SDB筛选工具。除了我们一年前报道的基于心率变异性（HRV）的SDB筛查方法外，我们还开发了一种基于ECG衍生呼吸（EDR）的新方法。该方法通过（a）测量单通道ECG中的峰谷QRS幅度，（b）消除由噪声和伪影引入的异常值，（c）内插导出的值和（d）过滤值来得出呼吸波形。在每分钟5和25个循环的呼吸速率内。然后每30秒将呼吸波形分类为正常，SDB或不确定时期。同时应用的先前报道的基于HRV的方法是基于在6分钟的滑动时间窗口内心率的功率谱，以对中间30秒的时间段进行分类。然后，我们结合了基于EDR和HRV的技术来优化每个时期的分类。组合的方法进一步提高了在带有注释SDB历元的独立测试数据库中SDB筛选的准确性。开发数据库来自PhysioNet（n = 25个多导睡眠图）。测试数据库来自波士顿的睡眠健康中心（n = 1907多导睡眠图），其中SDB历时（n = 1 538 222历时）是根据美国睡眠医学学会的标准进行评分的。第一个测试是对评估数据集中的每个时期进行分类。 EDR和HRV组合方法将78％的时期分为正常或SDB，不确定的为22％，计分的时期的总准确度为88％（不确定）。第二项测试是评估每位患者的SDB状态。该算法正确分类了71％的中重度SDB或轻度至无SDB。我们认为，基于ECG的方法为家庭和医院环境中的SDB筛查提供了一种有效且廉价的工具，并使SDB筛查在大范围人群中可行。

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