Niederhauser Thomas, Wyss-Balmer Thomas, Haeberlin Andreas, Marisa Thanks, Wildhaber Reto A, Goette Josef, Jacomet Marcel, Vogel Rolf
IEEE Trans Biomed Eng. 2015 Jun;62(6):1576-84. doi: 10.1109/TBME.2015.2395456. Epub 2015 Feb 6.
Long-term electrocardiogram (ECG) often suffers from relevant noise. Baseline wander in particular is pronounced in ECG recordings using dry or esophageal electrodes, which are dedicated for prolonged registration. While analog high-pass filters introduce phase distortions, reliable offline filtering of the baseline wander implies a computational burden that has to be put in relation to the increase in signal-to-baseline ratio (SBR). Here, we present a graphics processor unit (GPU)-based parallelization method to speed up offline baseline wander filter algorithms, namely the wavelet, finite, and infinite impulse response, moving mean, and moving median filter. Individual filter parameters were optimized with respect to the SBR increase based on ECGs from the Physionet database superimposed to autoregressive modeled, real baseline wander. A Monte-Carlo simulation showed that for low input SBR the moving median filter outperforms any other method but negatively affects ECG wave detection. In contrast, the infinite impulse response filter is preferred in case of high input SBR. However, the parallelized wavelet filter is processed 500 and four times faster than these two algorithms on the GPU, respectively, and offers superior baseline wander suppression in low SBR situations. Using a signal segment of 64 mega samples that is filtered as entire unit, wavelet filtering of a seven-day high-resolution ECG is computed within less than 3 s. Taking the high filtering speed into account, the GPU wavelet filter is the most efficient method to remove baseline wander present in long-term ECGs, with which computational burden can be strongly reduced.
长期心电图（ECG）常常受到相关噪声的干扰。特别是在使用干式或食管电极进行长时间记录的心电图中，基线漂移尤为明显。虽然模拟高通滤波器会引入相位失真，但对基线漂移进行可靠的离线滤波意味着需要承担一定的计算负担，这必须与信号与基线比率（SBR）的增加相权衡。在此，我们提出一种基于图形处理器单元（GPU）的并行化方法，以加速离线基线漂移滤波算法，即小波、有限和无限脉冲响应、移动平均和移动中值滤波器。基于Physionet数据库中的心电图叠加到自回归建模的真实基线漂移，针对SBR的增加对各个滤波器参数进行了优化。蒙特卡罗模拟表明，对于低输入SBR，移动中值滤波器优于任何其他方法，但会对心电图波形检测产生负面影响。相比之下，在高输入SBR的情况下，无限脉冲响应滤波器更受青睐。然而，并行化的小波滤波器在GPU上分别比这两种算法快500倍和4倍，并且在低SBR情况下提供了卓越的基线漂移抑制效果。使用64兆样本的信号段作为一个整体进行滤波，对七天的高分辨率心电图进行小波滤波计算不到3秒。考虑到高滤波速度，GPU小波滤波器是去除长期心电图中基线漂移的最有效方法，使用它可以大大减轻计算负担。
