Howtosignalprocessing
- How do you find the dominant frequency of a signal?
- How can I make my FFT more accurate?
- What are the limitations of FFT?
- How do you normalize FFT?
How do you find the dominant frequency of a signal?
Dominant frequency is defined as the highest magnitude sinusoidal component of the electrogram and is found by decomposing the electrograms into a finite number of sinusoidal constituents and finding the one with the highest magnitude.
How can I make my FFT more accurate?
The most intuitive way to increase the frequency resolution of an FFT is to increase the size while keeping the sampling frequency constant. Doing this will increase the number of frequency bins that are created, decreasing the frequency difference between each.
What are the limitations of FFT?
A disadvantage associated with the FFT is the restricted range of waveform data that can be transformed and the need to apply a window weighting function (to be defined) to the waveform to compensate for spectral leakage (also to be defined). An alternative to the FFT is the discrete Fourier transform (DFT).
How do you normalize FFT?
Normalise the fft by dividing it by the length of the original signal in the time domain. Zero values within the signal are considered to be part of the signal, so 'non-zero samples' is inappropriate. The length to use to normalise the signal is the length before adding zero-padding.
