A peak is a peak. If your data has many peaks in it, can findpeaks really know that? At the same time, findpeaks is the correct tool to use. You just need to smooth out the noise, FIRST. So that means you need to use a tool that can smooth your curve in advance. smooth is a good choice, because it has multiple methods to perform the smoothing, and you can make the choice yourself.
help smooth
SMOOTH Smooth data.
Z = SMOOTH(Y) smooths data Y using a 5-point moving average.
Z = SMOOTH(Y,SPAN) smooths data Y using SPAN as the number of points used
to compute each element of Z.
Z = SMOOTH(Y,SPAN,METHOD) smooths data Y with specified METHOD. The
available methods are:
'moving' - Moving average (default)
'lowess' - Lowess (linear fit)
'loess' - Loess (quadratic fit)
'sgolay' - Savitzky-Golay
'rlowess' - Robust Lowess (linear fit)
'rloess' - Robust Loess (quadratic fit)
Z = SMOOTH(Y,METHOD) uses the default SPAN 5.
Z = SMOOTH(Y,SPAN,'sgolay',DEGREE) and Z = SMOOTH(Y,'sgolay',DEGREE)
additionally specify the degree of the polynomial to be used in the
Savitzky-Golay method. The default DEGREE is 2. DEGREE must be smaller
than SPAN.
Z = SMOOTH(X,Y,...) additionally specifies the X coordinates. If X is
not provided, methods that require X coordinates assume X = 1:N, where
N is the length of Y.
Notes:
1. When X is given and X is not uniformly distributed, the default method
is 'lowess'. The 'moving' method is not recommended.
2. For the 'moving' and 'sgolay' methods, SPAN must be odd.
If an even SPAN is specified, it is reduced by 1.
3. If SPAN is greater than the length of Y, it is reduced to the
length of Y.
4. In the case of (robust) lowess and (robust) loess, it is also
possible to specify the SPAN as a percentage of the total number
of data points. When SPAN is less than or equal to 1, it is
treated as a percentage.
For example:
Z = SMOOTH(Y) uses the moving average method with span 5 and
X=1:length(Y).
Z = SMOOTH(Y,7) uses the moving average method with span 7 and
X=1:length(Y).
Z = SMOOTH(Y,'sgolay') uses the Savitzky-Golay method with DEGREE=2,
SPAN = 5, X = 1:length(Y).
Z = SMOOTH(X,Y,'lowess') uses the lowess method with SPAN=5.
Z = SMOOTH(X,Y,SPAN,'rloess') uses the robust loess method.
Z = SMOOTH(X,Y) where X is unevenly distributed uses the
'lowess' method with span 5.
Z = SMOOTH(X,Y,8,'sgolay') uses the Savitzky-Golay method with
span 7 (8 is reduced by 1 to make it odd).
Z = SMOOTH(X,Y,0.3,'loess') uses the loess method where span is
30% of the data, i.e. span = ceil(0.3*length(Y)).
See also SPLINE.
Documentation for smooth
doc smooth
Other uses of smooth
curvefit/smooth ssm/smooth trackingABF/smooth
dssm/smooth statespace/smooth trackingIMM/smooth
msVAR/smooth
Which method is correct for you? The best thing is to try out a few, and see which one makes you happy. Really, it will not be the specific methoid you choose, but how wide you make the window or span of that method. And that is dependent on how much noise is in your data. So noiser data will require a longer span to help to smooth out the noise and thus to elmininate the spurious bumps and crap. Of course, if you make the span too large, then your peaks will become brodened out more than you want. So this will be a tradeoff, and we don't actually have your data to give an example specific to your problem. In turn, all that allows me to do is suggest you smooth your data using perhaps smooth.
Other methods like a spline fit are less likely to be good choices, because your curve seems to have sharp transitions in slope at the peaks. And spline hate singularities, even singularities in the derivative. That means you want to use a simpler low order method, like the ones you will find in smooth.
So just smooth your data, THEN call findpeaks. If you are finding too many spurious peaks, then you needed to smooth your data a little more strongly.
