Feature Extraction of FFT for One Class SVM
Assuming you have epochs/segments of data. For this kind of signals, it is a safe approach to extract features using wavelet representations.

Using FFT might work as well, but I dont know how problematic would be the stationarity assumption in this kind of applications. Besides, FFT estimates for this kind of signals is sometimes very noisy. If you insist on this kind of representation, I would suggest estimating the power spectral density using Welch or multitaper methods. These usually deliver a nicer spectral estimation.

In any case, using any spectral features as direct input to your SVM might not be the best idea. Irrespectively of what features you extract (FFT, Wavelets, etc..) I would include a data dimensionality reduction step (PCA would be the first thing I'd try)