Paul J S, Reddy M R, Kumar V J
Department of Electrical Engineering, Indian Institute of Technology, Madras, India.
Comput Biol Med. 1998 Nov;28(6):639-58. doi: 10.1016/s0010-4825(98)00042-0.
The signal processing steps for the analysis of stress ECGs are aimed at improving the signal to noise ratio (SNR) of recordings in addition to eliminating artifacts due to respiration, movement of arms, etc. In this paper, we bring forth two important applications of the discrete cosine transform (DCT) for noise suppression and removal of baseline wander. The noise suppression algorithm has been framed on the basis of a two step procedure involving singular value decomposition (SVD) smoothing operation in transform domain followed by that in time domain. The mean square error (MSE) resulting from the first step is shown to effectively follow the trend obtained by using an ideal Wiener filter using DCT. In the second step, the degree of closeness to the minimum mean square error (MMSE) of the ideal Wiener filter is improved by subjecting the filtered outputs to a second SVD smoothing operation in time domain. Application of this scheme to noisy records has resulted in near perfect reproduction of the original noise free ECG without significant alterations in its morphological features.
分析应激心电图的信号处理步骤旨在提高记录的信噪比（SNR），同时消除因呼吸、手臂运动等产生的伪迹。在本文中，我们提出离散余弦变换（DCT）在噪声抑制和基线漂移去除方面的两个重要应用。噪声抑制算法基于两步过程构建，第一步是在变换域进行奇异值分解（SVD）平滑操作，然后在时域进行。第一步得到的均方误差（MSE）被证明能有效跟踪使用DCT的理想维纳滤波器所获得的趋势。在第二步中，通过对滤波后的输出在时域进行第二次SVD平滑操作，提高了与理想维纳滤波器的最小均方误差（MMSE）的接近程度。将该方案应用于有噪声的记录，已实现对原始无噪声心电图的近乎完美再现，且其形态特征无明显改变。
