PeakFinder

Quickly finds local maxima (peaks) or minima (valleys) in a noisy signal.

Description

This function quickly finds local peaks or valleys (local extrema) in a noisy vector using a user defined magnitude threshold to determine if each peak is significantly larger (or smaller) than the data around it. The problem with the strictly derivative based peak finding algorithms is that if the signal is noisy many spurious peaks are found. However, more complex methods often take much longer for large data sets, require a large amount of user interaction, and still give highly variable results. This function attempts to use the alternating nature of the derivatives along with the user defined threshold to identify local maxima or minima in a vector quickly and robustly. The function is able to correctly identify the major peaks on a 1.5 million data point noisy sum of sinusoids in under a second as is shown in the example in the code comments.

Please don't hesitate to comment or contact me if you have suggestions about improvements that could be made to this function.

• MATLAB release: MATLAB 7.8 (R2009a)
• Other requirements: None
• Other Files: license.txt, ; peakfinder.m

[Morvan]

 

[Andres]

I was happily using peakfinder until I had severe trouble with a dataset beginning with repeated values.
I've boiled it down to a short example:

t = [49 49 54 49 -57 46 -96 -10 39 0];
startI1 = 1;
[Loc1, Peak1] = peakfinder(t(startI1:end),20);
startI2 = 2;
[Loc2, Peak2] = peakfinder(t(startI2:end),20);

figure
plot(t,'b.-')
hold on
plot(Loc1-1+startI1,Peak1,'ro')
plot(Loc2-1+startI2,Peak2,'go')

Problems:
1) The first call returns a wrong result (Loc1 = 2, Peak1 = 49).
2) The second call returns all three peaks, but the last one appears twice (Loc2 = [2 5 8 8].', Peak2 = [54 46 39 39].').

I hope these issues can be fixed as peakfinder works promisingly fast.
A minor suggestion: if the input is a row vector the output should be a row vector, too.

Regards
Andres

[Andres]

The bugs were fixed quickly. Thanks Nate!