Babaeizadeh Saeed, Zhou Sophia H, Pittman Stephen D, White David P
Advanced Algorithm Research Center, Philips Healthcare, Andover, MA 01810, USA.
J Electrocardiol. 2011 Nov-Dec;44(6):700-6. doi: 10.1016/j.jelectrocard.2011.08.004. Epub 2011 Sep 9.
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.
评估睡眠呼吸紊乱（SDB）（包括睡眠呼吸暂停）的方法多种多样，从简单的问卷调查到复杂的多通道多导睡眠图。基于心电图（ECG）的廉价且高效的解决方案可能填补这一空白，并提供一种新的SDB筛查工具。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心电图衍生呼吸（EDR）的新方法。该方法通过以下步骤得出呼吸波形：（a）测量单通道心电图中峰谷QRS波幅；（b）去除由噪声和伪迹引入的异常值；（c）对得出的值进行插值；（d）对每分钟5至25个周期呼吸频率范围内的值进行滤波。然后将每30秒的呼吸波形分类为正常、SDB或不确定时段。同时应用的先前报道的基于HRV的方法，是基于滑动6分钟时间窗口内心率的功率谱对中间30秒时段进行分类。然后，我们将基于EDR和HRV的技术相结合，以优化每个时段的分类。在一个带有注释SDB时段的独立测试数据库中，这种组合方法进一步提高了SDB筛查的准确性。开发数据库来自PhysioNet（n = 25份多导睡眠图）。测试数据库来自波士顿的睡眠健康中心（n = 1907份多导睡眠图），其中SDB时段（n = 1,538,222个时段）根据美国睡眠医学学会标准进行评分。第一次测试是对评估数据集中的每个时段进行分类。EDR和HRV组合方法将78%的时段分类为正常或SDB，22%分类为不确定，对于已评分时段（非不确定）的总准确率为88%。第二次测试是评估每位患者的SDB状态。该算法正确分类了71%患有中度至重度SDB或轻度至无SDB的患者。我们认为，基于心电图的方法为家庭和医院环境中的SDB筛查提供了一种高效且廉价的工具，并使在大量人群中进行SDB筛查成为可能。
