MATLAB:过滤嘈杂的心电图信号 [英] MATLAB: filter noisy EKG signal

问题描述

使用 matlab 从

What is the best filter to use to remove noise from an ECG signal with matlab?

解决方案

If you have access to the Signal Processing Toolbox, then check out the Savitzky-Golay filter, namely the function sgolay. There's an accompanying demo, just run sgolaydemo.

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The following is an example to show the various ways you can apply filtering and de-noising to a signal. Note some of these functions requires certain toolboxes to be present:

% load ecg: simulate noisy ECG
Fs=500;
x = repmat(ecg(Fs), 1, 8);
x = x + randn(1,length(x)).*0.18;

% plot noisy signal
figure
subplot(911), plot(x), set(gca, 'YLim', [-1 1], 'xtick',[])
title('noisy')

% sgolay filter
frame = 15;
degree = 0;
y = sgolayfilt(x, degree, frame);
subplot(912), plot(y), set(gca, 'YLim', [-1 1], 'xtick',[])
title('sgolayfilt')

% smooth
window = 30;
%y = smooth(x, window, 'moving');
%y = smooth(x, window/length(x), 'sgolay', 2);
y = smooth(x, window/length(x), 'rloess');
subplot(913), plot(y), set(gca, 'YLim', [-1 1], 'xtick',[])
title('smooth')

% moving average filter
window = 15;
h = ones(window,1)/window;
y = filter(h, 1, x);
subplot(914), plot(y), set(gca, 'YLim', [-1 1], 'xtick',[])
title('moving average')

% moving weighted window
window = 7;
h = gausswin(2*window+1)./window;
y = zeros(size(x));
for i=1:length(x)
    for j=-window:window;
        if j>-i && j<(length(x)-i+1) 
            %y(i) = y(i) + x(i+j) * (1-(j/window)^2)/window;
            y(i) = y(i) + x(i+j) * h(j+window+1);
        end
    end
end
subplot(915), plot( y ), set(gca, 'YLim', [-1 1], 'xtick',[])
title('weighted window')

% gaussian
window = 7;
h = normpdf( -window:window, 0, fix((2*window+1)/6) );
y = filter(h, 1, x);
subplot(916), plot( y ), set(gca, 'YLim', [-1 1], 'xtick',[])
title('gaussian')

% median filter
window = 15;
y = medfilt1(x, window);
subplot(917), plot(y), set(gca, 'YLim', [-1 1], 'xtick',[])
title('median')

% filter
order = 15;
h = fir1(order, 0.1, rectwin(order+1));
y = filter(h, 1, x);
subplot(918), plot( y ), set(gca, 'YLim', [-1 1], 'xtick',[])
title('fir1')

% lowpass Butterworth filter
fNorm = 25 / (Fs/2);               % normalized cutoff frequency
[b,a] = butter(10, fNorm, 'low');  % 10th order filter
y = filtfilt(b, a, x);
subplot(919), plot(y), set(gca, 'YLim', [-1 1])
title('butterworth')

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