Analysis of Nonstationary and Multicomponent Signals

Abstract

This chapter develops methods to study the dynamic characteristics of nonstationary and multicomponent biomedical signals and proposes schemes to apply the well-established Fourier transform and autoregressive-modeling techniques to analyze and parameterize nonstationary and multicomponent signals. Fixed or adaptive segmentation of the signals into quasistationary segments is one approach to facilitate the analysis of such signals using traditional techniques. Several approaches for segmentation have been presented in the chapter. Adaptive segmentation facilitates not only the identification of distinct and separate events at unknown time instants in the given signal, but also the characterization of events of variable duration using the same number of parameters. The chapter also presents advanced and recently developed techniques such as wavelets and time-frequency distributions that permit the analysis of nonstationary and multicomponent biomedical signals without segmentation, and blind source separation techniques that can help separate mixed and overlapping components. The results obtained using the techniques studied in this chapter provide advantages in pattern classification as well as efficient representation and analysis of signals.