Sadr Nadi, Jayawardhana Madhuka, de Chazal Philip
Annu Int Conf IEEE Eng Med Biol Soc. 2017 Jul;2017:1551-1554. doi: 10.1109/EMBC.2017.8037132.
A measure of the respiratory effort during a sleep study is an important contributor to the diagnosis of sleep apnoea. A common way of measuring respiratory effort is with bands with stretch sensors placed around the chest and/or abdomen. An alternative, and more convenient method from the patient's perspective, is via the ECG derived respiration (EDR) signal which provides an estimate of the respiratory effort at each heartbeat. In this study we performed a side-by-side comparison of the discrimination information for identifying epochs of sleep apnoea contained in the chest respiratory effort signal and three methods of calculating the EDR signal. Using simultaneously recorded chest band and ECG signals extracted from overnight polysomnogram (PSG) data from 8 subjects (4 controls, 4 apnoeas. MIT PhysioNet Apnea-ECG database), we extracted identical features from the two sensors and used the features to train a linear discriminant classifier to classify one-minute epochs as being apneic or normal. Ground truth labelling of each epoch was achieved with an expert using the full PSG as a reference. Our cross validation results revealed that the full respiratory effort signal resulted in an accuracy of 87% in correctly identifying the epoch label. When the respiratory signal was resampled at each heartbeat (as occurs with the EDR signal) the accuracy was 86%, suggesting that the sampling process inherent to the EDR signal does not materially affect its discrimination ability. The best EDR method was based on the calculating the QRS area for every heart and achieved an accuracy of 81%. Our results suggest that, while there is some information loss in the EDR estimation process, the EDR signal is a convenient and useful signal for sleep apnoea diagnosis.
在睡眠研究中，测量呼吸努力程度是诊断睡眠呼吸暂停的一个重要因素。测量呼吸努力程度的一种常见方法是使用带有拉伸传感器的带子环绕胸部和/或腹部。从患者角度来看，另一种更便捷的方法是通过心电图衍生呼吸（EDR）信号，该信号可估算每次心跳时的呼吸努力程度。在本研究中，我们对胸部呼吸努力信号中用于识别睡眠呼吸暂停时段的判别信息与三种计算EDR信号的方法进行了并列比较。利用从8名受试者（4名对照者、4名呼吸暂停患者。麻省理工学院生理网呼吸暂停 - 心电图数据库）的夜间多导睡眠图（PSG）数据中同时记录的胸部带子信号和心电图信号，我们从这两种传感器中提取了相同特征，并使用这些特征训练线性判别分类器，以将一分钟时段分类为呼吸暂停或正常。每位专家以完整的PSG作为参考对每个时段进行了真实标签标注。我们的交叉验证结果显示，完整的呼吸努力信号在正确识别时段标签方面的准确率为87%。当呼吸信号在每次心跳时进行重采样（如EDR信号那样），准确率为86%，这表明EDR信号固有的采样过程不会对其判别能力产生实质性影响。最佳的EDR方法是基于计算每个心跳的QRS面积，其准确率为81%。我们的结果表明，虽然在EDR估计过程中存在一些信息损失，但EDR信号对于睡眠呼吸暂停诊断而言是一种方便且有用的信号。
