Xue Q, Hu Y H, Tompkins W J
Department of Electrical and Computer Engineering, University of Wisconsin, Madison 53706.
IEEE Trans Biomed Eng. 1992 Apr;39(4):317-29. doi: 10.1109/10.126604.
We have developed an adaptive matched filtering algorithm based upon an artificial neural network (ANN) for QRS detection. We use an ANN adaptive whitening filter to model the lower frequencies of the ECG which are inherently nonlinear and nonstationary. The residual signal which contains mostly higher frequency QRS complex energy is then passed through a linear matched filter to detect the location of the QRS complex. We developed an algorithm to adaptively update the matched filter template from the detected QRS complex in the ECG signal itself so that the template can be customized to an individual subject. This ANN whitening filter is very effective at removing the time-varying, nonlinear noise characteristic of ECG signals. Using this novel approach, the detection rate for a very noisy patient record in the MIT/BIH arrhythmia database is 99.5%, which compares favorably to the 97.5% obtained using a linear adaptive whitening filter and the 96.5% achieved with a bandpass filtering method.
我们开发了一种基于人工神经网络（ANN）的自适应匹配滤波算法用于QRS波检测。我们使用一个ANN自适应白化滤波器来模拟心电图中本质上非线性且非平稳的低频部分。然后，将主要包含高频QRS复合波能量的残余信号通过一个线性匹配滤波器来检测QRS复合波的位置。我们开发了一种算法，可根据心电图信号中检测到的QRS复合波自适应更新匹配滤波器模板，以便能针对个体对象定制模板。这种ANN白化滤波器在去除心电图信号随时间变化的非线性噪声特征方面非常有效。使用这种新方法，在麻省理工学院/贝斯以色列女执事医疗中心心律失常数据库中对噪声很大的患者记录的检测率为99.5%，这比使用线性自适应白化滤波器获得的97.5%以及带通滤波方法实现的96.5%更具优势。
