TY - JOUR

T1 - Quantifying self-similarity in cardiac inter-beat interval time series

AU - McSharry, P E

AU - Malamud, B D

PY - 2005

Y1 - 2005

N2 - We compare and quantify the scaling and long-range persistence (long memory) in time series using five different techniques: power-spectral, wavelet variance, semi-variograms, rescaled-range (R/S) and detrended fluctuation analysis. We apply these techniques to both normal and log-normal synthetic fractional noises and motions generated using the spectral method, where a normally distributed white noise is appropriately filtered such that its power-spectral density, S, depends upon frequency, f, according to S similar to f(-beta). Finally, we examine the long-range persistence of cardiac interbeat intervals. We find that for normal [N] and log-normal [LN] fractional noises: (1) power-spectral analysis does a reasonably good job at correctly quantifying the strength of long-range persistence for all beta [N] and beta > -0.5 [LN]; (2) semivariograms, 1.2 < beta < 2.5 [N and LN]; (3) rescaled range 0.0 < beta < 0.8 [N and LN]; (4) wavelet variance analysis all beta [N] and beta > -0.8 [LN]; (5) detrended fluctuation analysis -0.8 < beta < 2.2 [N] and -0.2 < beta < 2.2 [LN].

AB - We compare and quantify the scaling and long-range persistence (long memory) in time series using five different techniques: power-spectral, wavelet variance, semi-variograms, rescaled-range (R/S) and detrended fluctuation analysis. We apply these techniques to both normal and log-normal synthetic fractional noises and motions generated using the spectral method, where a normally distributed white noise is appropriately filtered such that its power-spectral density, S, depends upon frequency, f, according to S similar to f(-beta). Finally, we examine the long-range persistence of cardiac interbeat intervals. We find that for normal [N] and log-normal [LN] fractional noises: (1) power-spectral analysis does a reasonably good job at correctly quantifying the strength of long-range persistence for all beta [N] and beta > -0.5 [LN]; (2) semivariograms, 1.2 < beta < 2.5 [N and LN]; (3) rescaled range 0.0 < beta < 0.8 [N and LN]; (4) wavelet variance analysis all beta [N] and beta > -0.8 [LN]; (5) detrended fluctuation analysis -0.8 < beta < 2.2 [N] and -0.2 < beta < 2.2 [LN].

U2 - 10.1109/CIC.2005.1588136

DO - 10.1109/CIC.2005.1588136

M3 - Article

SN - 0276-6574

SP - 459

EP - 462

JO - Computing in Cardiology

JF - Computing in Cardiology

ER -