Brown Lewis F, Arunachalam Shivaram P
South Dakota State University, Brookings, SD.
Biomed Sci Instrum. 2009;45:59-64.
Respiration rate is a commonly measured physiological parameter for which several methods have been proposed for obtaining from the electrocardiogram (ECG). In this paper the authors present a new real-time algorithm for obtaining the ECG-derived respiration (EDR) signal. This algorithm utilizes a real-time baseline wander removal technique which is based on the repetitive backward subtraction of the estimated baseline from the ECG signal. The estimated baseline is interpolated from the ECG signal at midpoints between each detected R-wave. As each segment of the estimated baseline signal is subtracted from the ECG, a "flattened" ECG signal is produced for which the amplitude of each R-wave is analyzed. The respiration signal is estimated from the amplitude modulation of R-waves caused by breathing. The algorithm depends only on the ECG morphology, interpolation and subtraction and is functional in real-time. Testing of the algorithm was conducted in a pseudo real-time environment using MATLABTM, and test results are presented for simultaneously recorded ECG and respiration recordings from the PhysioNet/PhysioBank Fantasia database. Test data from a patient was chosen with particularly large baseline wander components to ensure the reliability of the algorithm under adverse ECG recording conditions. The algorithm yielded respiration rates of 4.4 breaths/min. for Fantasia patient record f2y10 and 13.3 breaths/min. for Fantasia patient record f2y06. These were in good agreement with the respiration rates of the simultaneously recorded respiration data provided in the Fantasia database thus confirming the efficacy of the algorithm.
呼吸率是一种常用的生理参数，人们已经提出了几种从心电图（ECG）中获取该参数的方法。在本文中，作者提出了一种用于获取心电图衍生呼吸（EDR）信号的新实时算法。该算法利用了一种实时基线漂移去除技术，该技术基于从ECG信号中反复向后减去估计基线。估计基线是在每个检测到的R波之间的中点处从ECG信号中插值得到的。当从ECG中减去估计基线信号的每一段时，就会产生一个“平坦化”的ECG信号，对其每个R波的幅度进行分析。呼吸信号是根据呼吸引起的R波幅度调制来估计的。该算法仅依赖于ECG形态、插值和减法，并且具有实时功能。使用MATLABTM在伪实时环境中对该算法进行了测试，并给出了来自PhysioNet/PhysioBank Fantasia数据库的同步记录的ECG和呼吸记录的测试结果。选择了一名患者的测试数据，该数据具有特别大的基线漂移成分，以确保算法在不利的ECG记录条件下的可靠性。该算法对于Fantasia患者记录f2y10得出的呼吸率为4.4次/分钟，对于Fantasia患者记录f2y06得出的呼吸率为13.3次/分钟。这些结果与Fantasia数据库中提供的同步记录的呼吸数据的呼吸率非常一致，从而证实了该算法的有效性。
