MATLAB: How to normalize this signal

eegfilternoisy signalpeakfinder

I have a noisy data set that I am trying to find peaks for (I am using the peakfinder function). The function works great when the baseline of the signal does not change, but when it does it no longer finds peaks well – I was thinking of filtering the data, but was unsure what kind of filter would actually help. Any help would be greatly appreciated. A sample below:

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I suggest a low-order bandpass filter, perhaps Butterworth 3 or 4. That will eliminate your baseline drift and most of the noise, making it much easier for findpeak to work.

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MATLAB: Finding peaks in EEG data

You forgot to attach your data, so we can't try anything with it. Attach it if you want better help. Otherwise, if you have the Signal Processing Toolbox, you can try medfilt(), sgolayfilt(), and findpeaks(). It sounds like you tried findpeaks() and say it doesn't work for some reason, though you did not specify why it failed or what parameters you used for it.

For the Savitzky-Golay filter, use it to smooth the signal and remove spikes. This gives you like a background that you can then subtract from the original signal to give a "spikes-only" signal. Then you can use a global threshold. You can use the same basic process with the median filter, or even conv() for that matter. It looks like spikes that are more than about 20 are legitimate spikes and those less than 20 are just noise, but you can play with that number to set it optimally.

MATLAB: Zeros on [b,a] output of butter filter

The filter is unstable. This can easily be remedied by converting to zero-pole-gain realization, and then second-order section implementation:

ftype = 'low';
fs = 32000
fn = fs/2;                                %(fs = 32000)
fc = 125/fn;                              %normalised passband frequency = 0.0078
fz = 135/fn;                              %normalised stopband frequency = 0.0084
[n, wn] = buttord(fc,fz,3,60);            %(n = 60, wn = 0.0078)
[z,p,k] = butter(n, wn, ftype);         % Use Zero-Pole-Gain Representation
[sos,g] = zp2sos(z,p,k);                % Second-Order Section For Stability
figure
freqz(sos, 2^14, fs)                        % Filter Bode Plot

Then use:

signal_filtered = filtfilt(sos,g,signal);

to do the actual filtering.

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MATLAB: Finding peaks in EEG data

MATLAB: Zeros on [b,a] output of butter filter

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