testecgkalmanfilter.m

用于心电信号滤波

%
% Test program for ECG filtering using EKF and EKS
%
% Dependencies: The baseline wander and ECG filtering toolboxes of the Open Source ECG Toolbox
%
% Open Source ECG Toolbox, version 1.0, November 2006
% Released under the GNU General Public License
% Copyright (C) 2006  Reza Sameni
% Sharif University of Technology, Tehran, Iran -- LIS-INPG, Grenoble, France
% [e-mail]

% This program is free software; you can redistribute it and/or modify it
% under the terms of the GNU General Public License as published by the
% Free Software Foundation; either version 2 of the License, or (at your
% option) any later version.
% This program is distributed in the hope that it will be useful, but
% WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
% Public License for more details. You should have received a copy of the
% GNU General Public License along with this program; if not, write to the
% Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston,
% MA  02110-1301, USA.

clc
clear all
close all;

%load('SampleECG1.mat');
load('SampleECG2.mat'); data = data(1:15000,6)';

fs = 1000;
t = [0:length(data)-1]/fs;

f = 1;                                          % approximate R-peak frequency

bsline = LPFilter(data,.7/fs);                  % baseline wander removal (may be replaced by other approaches)
%bsline = BaseLineKF(data,.5/fs);                % baseline wander removal (may be replaced by other approaches)

data1 = data - bsline;

%//////////////////////////////////////////////////////////////////////////
% Making the data noisy
SNR = 0
SignalPower = mean(data1.^2);
NoisePower = SignalPower / 10^(SNR/10);
x = data1 + sqrt(NoisePower)*randn(size(data1));
%x =  data1 + [NoiseGenerator(5,SignalPower,SNR,length(data1),fs,[1 1 1],0)]';
%//////////////////////////////////////////////////////////////////////////

peaks = PeakDetection(x,f/fs);                  % peak detection

[phase phasepos] = PhaseCalculation(peaks);     % phase calculation

teta = 0;                                       % desired phase shift
pphase = PhaseShifting(phase,teta);             % phase shifting

bins = 250;                                     % number of phase bins
[ECGmean,ECGsd,meanphase] = MeanECGExtraction(x,pphase,bins,1); % mean ECG extraction 

ECGBeatFitter(ECGmean,meanphase);           % ECG beat fitter GUI

%//////////////////////////////////////////////////////////////////////////
N = length(OptimumParams)/3;% number of Gaussian kernels
JJ = find(peaks);
fm = fs./diff(JJ);          % heart-rate
w = mean(2*pi*fm);          % average heart-rate in rads.
wsd = std(2*pi*fm,1);       % heart-rate standard deviation in rads.

y = [phase x'];

X0 = [-pi 0]';
P0 = [(2*pi)^2 0 ;0 (10*max(abs(x))).^2];
Q = diag( [ (.1*OptimumParams(1:N)).^2 (.05*ones(1,N)).^2 (.05*ones(1,N)).^2 (wsd)^2 , (.05*mean(ECGsd(1:round(end/10))))^2] );
R = [(w/fs).^2/12 0 ;0 (mean(ECGsd(1:round(end/10)))).^2];
Wmean = [OptimumParams w 0]';
Vmean = [0 0]';
Inits = [OptimumParams w fs];

InovWlen = ceil(.5*fs);     % innovations monitoring window length
tau = [];                   % Kalman filter forgetting time. tau=[] for no forgetting factor
gamma = 1;                  % observation covariance adaptation-rate. 0<gamma<1 and gamma=1 for no adaptation
RadaptWlen = ceil(fs/2);    % window length for observation covariance adaptation

[Xekf,Phat,Xeks,PSmoothed,ak] = EKSmoother(y,X0,P0,Q,R,Wmean,Vmean,Inits,InovWlen,tau,gamma,RadaptWlen,1);

%//////////////////////////////////////////////////////////////////////////
figure
plot(t,x);
hold on;
plot(t,Xekf(:,2)','g');
plot(t,Xeks(:,2)','r');
plot(t,data1,'m');
grid;
legend('Noisy','EKF Output','EKS Output','Original ECG');

EKFsnr = 10*log10(mean(data1.^2)/mean((data1-Xekf(:,2)').^2))
EKSsnr = 10*log10(mean(data1.^2)/mean((data1-Xeks(:,2)').^2))