Abstract
The human heartbeat interval is determined by complex nerve control and environmental inputs. As a result, the heartbeat interval for a human is a complex time series, as shown by previous studies. Most of the analysis algorithms proposed for characterizing the profile of heartbeat time series, such as detrended fluctuation analysis and multi-scale entropy, are based on various characteristics of dynamics. In this study, we present an empirical mode decomposition-based intrinsic mode analysis, which uses the appearance energy index (AEI) to quantify the property of long-term correlation, and structure index (SI) to characterize the internal modulation of data. This presented algorithm was used to investigate the human heartbeat time series downloaded from PhysioBank. We found the profiles of human heartbeat time series of subjects with congestive heart failure (CHF) or atrial fibrillation (AF) are significantly different from those of healthy subjects in internal modulation as shown by SI. Moreover, AEI is the critical characteristics for verifying subjects with CHF from subjects with AF in a degree of long-term correlation. Both AEI and SI contribute to presenting the characteristic profiles of a human heartbeat time series.
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Acknowledgments
The authors wish to thank Prof. Ary L. Goldberger and Dr. C. K. Peng (the director and co-director of the Rey Institute for Nonlinear Dynamics in Medicine at the Beth Israel Deaconess Medical Center of Harvard Medical School) for valuable discussions. We gratefully acknowledge the support from National Science Council (NSC) of Taiwan (Grant number NSC96-2221-E-155-015-MY3-2) for this research.
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Associate Editor Berj L. Bardakjian oversaw the review of this article.
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Yeh, JR., Sun, WZ., Shieh, JS. et al. Intrinsic Mode Analysis of Human Heartbeat Time Series. Ann Biomed Eng 38, 1337–1344 (2010). https://doi.org/10.1007/s10439-010-9939-z
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DOI: https://doi.org/10.1007/s10439-010-9939-z