基于小波神经网络的心电信号滤波研究

Research on Electrocardiogram De-noising Algorithm Based on Wavelet Neural Networks

针对心电(ECG)信号检测中存在的主要噪声,本文研究了基于小波神经网络(WNN)的ECG信号滤波理论。提出一种通过WNN非线性逼近能力构建的针对ECG信号的非线性滤波器算法和滤波策略,实现对ECG信号中基线漂移、肌电干扰、工频干扰噪声的滤除;给出了网络训练算法和滤波实验,滤波后信号与期望信号误差范围在微伏级,验证了本文提出的基于WNN的心电非线性滤波器对心电主要噪声快速滤波的良好效果,最后讨论了影响WNN用于心电滤波的几个关键问题。

In this paper,the ECG de-noising technology based on wavelet neural networks（WNN） is used to deal with the noises in Electrocardiogram（ECG） signal.The structure of WNN,which has the outstanding nonlinear mapping capability,is designed as a nonlinear filter used for ECG to cancel the baseline wander,electromyo-graphical interference and powerline interference.The network training algorithm and de-noising experiments results are presented,and some key points of the WNN filter using ECG de-noising are discussed.