A comparison of fast Fourier transform (FFT) and autoregressive (AR) spectral estimation techniques for the analysis of tremor data.

This review outlines the theory of spectral estimation techniques based on the fast Fourier transform (FFT) and autoregressive (AR) model and their application to the analysis of human tremor data. Two FFT-based spectral estimation techniques are presented, the Blackman-Tukey and periodogram methods. Factors that influence the quality of spectral estimates are discussed including the choice of windowing function. The theory of parametric modelling is introduced and AR modelling identified as the technique best suited to the analysis of tremor data. The processes of parameter estimation and model order selection are described. The theory of AR spectral estimation is outlined and differences between the AR and FFT-based spectral estimates are summarised. A brief guide to the implementation of FFT-based and AR spectral estimation techniques is given concentrating on data analysis packages that require little or no programming expertise. This review concludes that the AR modelling approach can produce tremor spectra that are superior to those from FFT-based methods for short data sequences. Although the spectral estimates are improved, the benefits of AR modelling for providing information about the physiological mechanisms of tremor generation are not yet clear.