I enclose for you a paper that  include  applications of some frequency domain approaches for medical imaging.

Good Luck.

Prof. S. EL-Rabaie

Time-frequency analyses are good tools for biomedical signal processing. The simplest is the STFT and the other is VW (wigner-ville). The MATLAB toolbox for TF analysis is available which can be easily adopted for implementation.

Thank you prof.S. EL-Rabaie for your enclosed article..

thank you Aniket Kumar · and Hadi Zayyani for your valuable comments and time :)

Hello.

By biomedical signals, I suppose you mean nonstationary signals? A number of other factors could also be present, such as vector signals (array of sensors or electrodes), low signal-to-noise ratio, short observation interval, etc. If the main problem is nonstationarity, you need time-frequency analysis, as suggested by Hadi, above. The basic tool is the FFT applied over a window, usually with time-domain tapering (windowing) and averaging, which is called STFT. There are other techniques such as wavelets and more general transforms like Wigner Ville, but STFT is the easiest to do and very fast due to the use of the FFT. You can also apply this to vector or array sensors (as in sonar and radar signal processing). I would always plot the spectrogram  from the STFT first as it's so easy to interpret.. The STFT is also easy to implement on a DSP.

If you have a short data segment, you might need to allow for initial transients, in which case you can try a 2D power spectral density. A reference is attached.

Article The Two-Dimensional Power Spectral Density: A Connection Bet...