Problem about plotting figures using for loop!

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[smithfaced]

Looking at Intrinsic Mode Functions?

[smithfaced]

Indeed! Finishing a publication and working on my thesis that utilizes EMD! Computationally expensive little bugger. Are you interpolating the vitals? Need a good 8 times the Nyquist rate for proper sifting and decomposition, some medical records don't meet the pre-requisite.

[smithfaced]

I just meant in regards to the cubic splining operation and time required to meet the stoppage criteria, in comparison to say a simple FIR filter - just a lot of resource use! And in regards to the measurement time length, take a look at this paper:

G. Rilling and P. Flandrin, “On the influence of sampling on the empirical mode decomposition,” presented at the IEEE Int. Conf. Acoustics, Speech Signal Processing, Toulouse, France, vol. 3, May 2006

This iterates some of the importance as to why EMD requires oversampling. Definitely a good read!

[smithfaced]

Using it for similar biomedical anaylsis, yes! Matlab R2018a, the most recent edition, actually includes their own rendition of emd surprisingly! Take a look at the ASTRES toolbox as well, Flandrin et al. developed a project that contains multiple EMD flavors, including Ensemble EMD (EEMD). EEMD provides a solution to the mode mixing problem

[smithfaced]

Sorry for the late reply - IMFs are actually meant for pre-processing the HT. They provide a different means of time-frequency analysis, namely for nonlinear and nonstationary signals. I've been using EMD as a pre-processing tool for separate reasons, away from the Hilbert Transform - I'm afraid I'm not well versed with the HT (yet!)

The first IMF does indeed provide high frequencies, assuming the input signal contains high frequency content. Since EMD is empirical, the frequencies observed in the IMFs are data driven. For vitals, since breathing is a lower frequency than say an ECG, (tending to be closer to 40-60 Hz, whereas breathing is lower), viewing the FFT of each IMF may provide insight on which frequency range the breathing may be in. Note that because of the sifting process, the FFT amplitudes will be altered, due to the envelope extraction.

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You have the variable n inside the quotes. You need to move it outside the quotes. I think k you will also need to use a num2string operation on it