Brown Lewis F, Arunachalam Shivaram P
South Dakota State University, Brookings, SD.
Biomed Sci Instrum. 2009;45:65-70.
The electrocardiogram (ECG) is often contaminated with various noises including electromyographic, 60 Hz, respiratory and baseline wander (BW). The BW noise presents challenges in removal from the ECG by conventional filtering approaches because its frequency content overlaps with that of ECG signals. Removal of the BW noise is often preferred as a step before ECG signal processing. In this paper we present an algorithm for estimating and removing BW noise from a single-channel ECG signal, which can be implemented in real-time digital signal processing hardware and software. The algorithm uses the Pan & Tompkins R-wave detection method and places an interpolation point (i.e., a "T-P knot") at each R-R midpoint. It performs a cubic spline interpolation of the four most recently detected T-P knots to estimate the most recent segment of the BW noise. This most recent segment is then subtracted from the ECG signal to produce a "flattened" signal. The algorithm was implemented and tested in a pseudo real-time environment using MATLABTM, and test results are presented for simulated ECG and BW data as well as for actual ECG recordings from the PhysioNet/PhysioBank Fantasia database containing very large BW signal components. Correlations of 0.9959-0.9978 are shown for the estimated versus actual BW signals confirming the accuracy of the T-P knot algorithm.
心电图（ECG）常常受到各种噪声的干扰，包括肌电图、60Hz、呼吸和基线漂移（BW）。基线漂移噪声通过传统滤波方法从心电图中去除存在挑战，因为其频率成分与心电图信号的频率成分重叠。在进行心电图信号处理之前，通常首选去除基线漂移噪声。在本文中，我们提出了一种从单通道心电图信号中估计和去除基线漂移噪声的算法，该算法可在实时数字信号处理硬件和软件中实现。该算法使用潘氏和汤普金斯R波检测方法，并在每个R-R中点处设置一个插值点（即“T-P节点”）。它对最近检测到的四个T-P节点进行三次样条插值，以估计基线漂移噪声的最新段。然后从心电图信号中减去该最新段，以产生一个“平坦化”的信号。该算法在使用MATLABTM的伪实时环境中实现并进行了测试，并给出了模拟心电图和基线漂移数据以及来自PhysioNet/PhysioBank Fantasia数据库包含非常大基线漂移信号成分的实际心电图记录的测试结果。估计的基线漂移信号与实际基线漂移信号的相关性显示为0.9959 - 0.9978，证实了T-P节点算法的准确性。
