MATLAB: Adaptive filtering by using uncorrelativity feature

adaptive filteringcorrelationecg

Hi every body….. I have three signals , two of them are supposed to be correlated , while the the third is supposed to be uncorrelated with the first two signals , as I know that for two signals to be uncorrelated , their mean values should be zero, am I wrong? there is a program in matlab help that perform adaptive filtering on ECG signal to extract fetal ECG , but I tried to measure the correlativity between them I found that it does not match what I know about the principle of correlation , please help me to understand that principle….thank you for your patience….

Best Answer

The cross correlation function of two signals is calculated using the zero-mean/unit-variance zscores. Therefore it is invariant w.r.t. changes in means and standard deviations.

Although the functions XCORR, CROSSCORR and NNCORR automatically calculate the zscores, the zscores must be explicitly calculated when obtaining the cross correlation using FFT.

Hope this helps.

Greg

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MATLAB: Too high correlation value from xcorr and corrcoef for uncorelated sequences

Ah, but there is quite a lot of correlation; remember it's not the magnitude of the values that matters, it's only whether they tend to "move together". To see this, try

figure, subplot(2,1,1)
yyaxis left, plot(dat(:,1))
yyaxis right,plot(dat(:,2))
ylim([0.35 1.5])

to overlay the two data series on top of each other at a scale factor that makes them roughly match each other in mean amplitude. What this shows is pretty much the same overall trend and that even some of the substructure is similar; particularly there being a drop towards the RH end around 5000. But, mostly what the correlation is measuring is the overall trend.

To see this,

subplot(2,1,2)
dtr=detrend(dat);
plot(dtr)
corr=corrcoef(dtr(:,1),dtr(:,2))
corr =
  1.0000    0.1071
  0.1071    1.0000

which is probably much more like what you were expecting.

MATLAB: Convert White noise added ECG signal into binary

Load ECG Signal

load('mit200.mat')

Make it all positive

shift_up = min(ecgsig);
ecg_n = ecgsig-shift_up;

Normalize it (0 to 1)

norm_1 = max(ecg_n);
ecg_n = ecg_n/norm_1;

convert it to I32

ecg_i32 = uint32(round(ecg_n*2^32));

Now your signal is converted in u32, you can use de2bi

ecg_b = de2bi(ecg_u32,32);

The signal ecg_b is the binary for of your ecg signal

To convert it back to normal

ecg_d = double(bi2de(ecg_b));

divide it by 2^32

ecg_d = ecg_d/2^32;

Now the signal you ve is normalized between 0 and 1. to convert it to original range

ecg_d = ecg_d*norm_1;
ecg_d = ecg_d+shift_up;

Best Answer

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MATLAB: Too high correlation value from xcorr and corrcoef for uncorelated sequences

MATLAB: Convert White noise added ECG signal into binary

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