SCALING BEHAVIOR OF HEARTBEAT INTERVALS OBTAINED BY WAVELET-BASED TIME-SERIES ANALYSIS

BIOLOGICAL time-series analysis is used to identify hidden dynamical p atterns which could yield important insights into underlying physiolog ical mechanisms, Such analysis is complicated by the fact that biologi cal signals are typically both highly irregular and non-stationary, th at is, their statistical character changes slowly or intermittently as a result of variations in background influences(1-3). Previous statis tical analyses of heart beat dynamics(4-6) have identified long-range correlations and power-law scaling in the normal heartbeat, but not th e phase interactions between the different frequency components of the signal, Here we introduce a new approach, based on the wavelet transf orm and an analytic signal approach, which can characterize non-statio nary behaviour and elucidate such phase interactions, We find that, wh en suitably rescaled, the distributions of the variations in the beat- to-beat intervals for all healthy subjects are described by a single f unction stable over a Hide range of timescales. However, a similar sca ling function does not exist for a group with cardiopulmonary instabil ity caused by sleep apnoea. We attribute the functional form of the sc aling observed in the healthy subjects to underlying nonlinear dynamic s, which seem to be essential to normal heart function, The approach i ntroduced here should be useful in the analysis of other nonstationary biological signals.