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

P E McSharry, B D Malamud

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)


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].

Original languageEnglish
Pages (from-to)459-462
Number of pages4
JournalComputing in Cardiology
Publication statusPublished - 2005


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