Philips W
Department of Electronics and Information Systems, University of Gent, Belgium.
IEEE Trans Biomed Eng. 1996 May;43(5):480-92. doi: 10.1109/10.488796.
This paper presents the time-warped polynomial filter (TWPF), a new interval-adaptive filter for removing stationary noise from nonstationary biomedical signals. The filter fits warped polynomials to large segments of such signals. This can be interpreted as low-pass filtering with a time-varying cut-off frequency. In optimal operation, the filter's cut-off frequency equals the local signal bandwidth. However, the paper also presents an iterative filter adaptation algorithm, which does not rely on the (complicated) computation of the local bandwidth. The TWPF has some important advantages over existing adaptive noise removal techniques: it reacts immediately to changes in the signal's properties, independently of the desired noise reduction; it does not require a reference signal and can be applied to nonperiodical signals. In case of quasiperiodical signals, applying the TWPF to the individual signal periods leads to an optimal noise reduction. However, the TWPF can also be applied to intervals of fixed size, at the expense of a slightly lower noise reduction. This is the way nonquasiperiodical signals are filtered. The paper presents experimental results which demonstrate the usefulness of the interval-adaptive filter in several biomedical applications: noise removal from ECG, respiratory and blood pressure signals, and base line restoration of electro-encephalograms (EEG's).
本文介绍了时间扭曲多项式滤波器（TWPF），这是一种用于从非平稳生物医学信号中去除平稳噪声的新型区间自适应滤波器。该滤波器将扭曲多项式拟合到此类信号的大段数据上。这可以解释为具有时变截止频率的低通滤波。在最佳操作中，滤波器的截止频率等于局部信号带宽。然而，本文还提出了一种迭代滤波器自适应算法，该算法不依赖于局部带宽的（复杂）计算。与现有的自适应噪声去除技术相比，TWPF具有一些重要优势：它能立即对信号特性的变化做出反应，与期望的降噪无关；它不需要参考信号，并且可以应用于非周期性信号。对于准周期性信号，将TWPF应用于各个信号周期可实现最佳降噪。然而，TWPF也可以应用于固定大小的区间，代价是降噪效果略低。这就是对非准周期性信号进行滤波的方式。本文给出的实验结果表明了区间自适应滤波器在多种生物医学应用中的实用性：从心电图、呼吸和血压信号中去除噪声，以及脑电图（EEG）的基线恢复。
