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基于小波神经网络的心电图去噪算法研究

[Research on electrocardiogram de-noising algorithm based on wavelet neural networks].

作者信息

Wan Xiangkui, Zhang Jun

机构信息

Information Engineering College, Guangdong University of Technology, Guangzhou 510006, China.

出版信息

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2010 Dec;27(6):1197-201.

Abstract

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.

摘要

本文采用基于小波神经网络(WNN)的心电图去噪技术处理心电图(ECG)信号中的噪声。具有出色非线性映射能力的WNN结构被设计为用于心电图的非线性滤波器,以消除基线漂移、肌电干扰和电力线干扰。给出了网络训练算法和去噪实验结果,并讨论了使用WNN滤波器进行心电图去噪的一些关键点。

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