Wu Yunfeng, Rangayyan Rangaraj M, Zhou Yachao, Ng Sin-Chun
School of Information Engineering, Beijing University of Posts and Telecommunications, 10 Xi Tu Cheng Road, Haidian District, Beijing 100876, China.
Med Eng Phys. 2009 Jan;31(1):17-26. doi: 10.1016/j.medengphy.2008.03.004. Epub 2008 May 9.
We present a novel unbiased and normalized adaptive noise reduction (UNANR) system to suppress random noise in electrocardiographic (ECG) signals. The system contains procedures for the removal of baseline wander with a two-stage moving-average filter, comb filtering of power-line interference with an infinite impulse response (IIR) comb filter, an additive white noise generator to test the system's performance in terms of signal-to-noise ratio (SNR), and the UNANR model that is used to estimate the noise which is subtracted from the contaminated ECG signals. The UNANR model does not contain a bias unit, and the coefficients are adaptively updated by using the steepest-descent algorithm. The corresponding adaptation process is designed to minimize the instantaneous error between the estimated signal power and the desired noise-free signal power. The benchmark MIT-BIH arrhythmia database was used to evaluate the performance of the UNANR system with different levels of input noise. The results of adaptive filtering and a study on convergence of the UNANR learning rate demonstrate that the adaptive noise-reduction system that includes the UNANR model can effectively eliminate random noise in ambulatory ECG recordings, leading to a higher SNR improvement than that with the same system using the popular least-mean-square (LMS) filter. The SNR improvement provided by the proposed UNANR system was higher than that provided by the system with the LMS filter, with the input SNR in the range of 5-20 dB over the 48 ambulatory ECG recordings tested.
我们提出了一种新型的无偏归一化自适应降噪（UNANR）系统，用于抑制心电图（ECG）信号中的随机噪声。该系统包含以下步骤：使用两阶段移动平均滤波器去除基线漂移，使用无限脉冲响应（IIR）梳状滤波器对电力线干扰进行梳状滤波，使用加性白噪声发生器根据信噪比（SNR）测试系统性能，以及使用UNANR模型估计从受污染的ECG信号中减去的噪声。UNANR模型不包含偏置单元，其系数通过最速下降算法进行自适应更新。相应的自适应过程旨在最小化估计信号功率与期望无噪声信号功率之间的瞬时误差。使用基准MIT-BIH心律失常数据库评估了UNANR系统在不同输入噪声水平下的性能。自适应滤波结果以及对UNANR学习率收敛性的研究表明，包含UNANR模型的自适应降噪系统能够有效消除动态心电图记录中的随机噪声，与使用流行的最小均方（LMS）滤波器的同一系统相比，能带来更高的SNR提升。在所测试的48份动态心电图记录中，当输入SNR在5 - 20 dB范围内时，所提出的UNANR系统提供的SNR提升高于使用LMS滤波器的系统。
