A multiscale entropy method correctly classified 96% (48 of 50) of RR time series as physiologic or synthetic, and 100% when combined with Fourier spectral analysis.
Does a multiscale entropy method accurately distinguish physiologic from synthetic RR time series?
Multiscale entropy, especially combined with Fourier spectral analysis, is highly effective at distinguishing physiologic from synthetic RR time series.
We address the challenge of distinguishing physiologic interbeat interval time series from those generated by synthetic algorithms via a newly developed multiscale entropy method. Traditional measures of time series complexity only quantify the degree of regularity on a single time scale. However, many physiologic variables, such as heart rate, fluctuate in a very complex manner and present correlations over multiple time scales. We have proposed a new method to calculate multiscale entropy from complex signals. In order to distinguish between physiologic and synthetic time series, we first applied the method to a learning set of RR time series derived from healthy subjects. We empirically established selected criteria characterizing the entropy dependence on scale factor for these datasets. We then applied this algorithm to the CinC 2002 test datasets. Using only the multiscale entropy method, we correctly classified 48 of 50 (96%) time series. In combination with Fourier spectral analysis, we correctly classified all time series.
Costa et al. (Wed,) conducted a other in RR time series classification (n=50). Multiscale entropy method was evaluated on Correct classification of physiologic vs synthetic time series. A multiscale entropy method correctly classified 96% (48 of 50) of RR time series as physiologic or synthetic, and 100% when combined with Fourier spectral analysis.
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