Abstract

: "Surrogate data" is the basis for a technique for testing a time series for nonlinear dynamics and process nonstationarities. The theory behind surrogate data is briefly described, along with an algorithm for generating it. Examples are given of its use in detecting nonlinearity in heart rate signals, detecting nonstationarity, and estimating the sampling distribution of complicated statistics of heart rate variability. 3.1 1. Introduction In studying heart rate and blood pressure variability, one is faced with a dilemma. The mechanisms regulating the cardiovascular system are known theoretically to contain many nonlinearities, and the system as a whole is known to be nonstationary. Yet the dominant techniques for analyzing time series --- for example, spectral analysis --- are based in the assumptions of linear dynamics and stationarity. The ultimate resolution to this dilemma may come in the form of improved time series analysis techniques that can cope optimally with nonlinearit...

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