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.

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 )

Two filter design tools/demos that you may want to check out:

• FDATool in the Signal Processing Toolbox (if you have access to it).

• Analog Filter Design Toolbox from James Squire on the MathWorks File Exchange. There appear to be simulations for fitering EKG data included in the toolbox.

These should give you a chance to try out different filters and filter parameters to see how they perform with EKG/ECG data.