目录
💥1 概述
📚2 运行结果
🎉3 参考文献
👨💻4 Matlab代码
💥1 概述
心电图（ECG）信号自动分析与诊断是目前信号处理领域中的研究热点之一,其真正实现将有力地促进医疗事业的发展和人们健康水平的提高,也将是现代信号处理理论与技术和人工智能等在医疗领域中应用的重大突破。心电（图）信号自动分析与诊断系统的研究内容广泛,涉及的基础理论和关键技术繁多,是一个多学科交叉的庞大课题。到目前为止,现有的心电信号自动分析方法中还存在着诸多的缺陷和不足,在理论研究和实际应用方面仍有许多改进和创新的空间。针对这种现状,论文围绕基本波检测、信号去噪、信号重建度量三个方面的关键技术展开研究。
📚2 运行结果
🎉3 参考文献
[1]牛传莉. 心电信号预处理和波形检测算法的研究[D].北京交通大学,2009.
👨💻4 Matlab代码
主函数部分代码：
clc
clear all
close all;
N = 10000; % number of samples
NumCh = 8; % number of channels
fs = 500; % sampling rate
% the ratio between the different types of noise
w_bw = 5; % weight of baseline wander noise in the generated noise (for noisetype = 5)
w_em = 1; % weight of electrode movement noise in the generated noise (for noisetype = 5)
w_ma = 1; % weight of muscle artifact noise in the generated noise (for noisetype = 5)
M = 10800;
% original noise template
template = NoiseGenerator('mixture',1,0,M,360,[w_bw,w_em,w_ma],1000);
% parameters required for estimating the AR coefficients using a Kalman Filter (KF)
order = 12; % AR model order for modeling the ECG noise
[a0,e] = aryule(template,order); % a global AR model
q = (.05*max(abs(a0(2:end))))^2; % AR coefficients covariance
R = 1; % unit variance noise
p0 = 1e6*q; % covariance of the KF initial state
alpha = 1; % KF forgetting factor
% time-variant AR parameter estimation using KF and Kalman Smoother (KS)
[Ahat,Asmoothed] = TimeVariantAR(template,order,a0(2:end)',q,R,p0,alpha);
% generating different instances of ECG noise using the time-variant AR parameters
noise1 = zeros(NumCh,N);
noise2 = zeros(NumCh,N);
for j = 1:NumCh,
x = randn(1,M);
y1 = zeros(M,1);
y2 = zeros(M,1);
for i = order+1:M-1,
y1(i) = (sqrt(1)*x(i)-Ahat(:,i)'*y1(i-1:-1:i-order))/1; % KF
y2(i) = (sqrt(1)*x(i)-Asmoothed(:,i)'*y2(i-1:-1:i-order))/1; % KS
end
