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 base d on the fast Fourier transform (FFT) and autoregressive (AR) model an d their application to the analysis of human tremor data. Two FFT-base d spectral estimation techniques are presented, the Blackman-Tukey and periodogram methods. Factors that influence the quality of spectral e stimates are discussed including the choice of windowing function. The theory of parametric modelling is introduced and AR modelling identif ied as the technique best suited to the analysis of tremor data. The p rocesses of parameter estimation and model order selection are describ ed. The theory of AR spectral estimation is outlined and differences b etween the AR and FFT-based spectral estimates are summarised. A brief guide to the implementation of FFT-based and AR spectral estimation t echniques is given concentrating on data analysis packages that requir e little or no programming expertise. This review concludes that the A R modelling approach can produce tremor spectra that are superior to t hose from FFT-based methods for short data sequences. Although the spe ctral estimates are improved, the benefits of AR modelling for providi ng information about the physiological mechanisms of tremor generation are not yet clear. (C) 1998 Elsevier Science B.V. All rights reserved .