Multiscale entropy analysis of biological signals.
@article{Costa2005MultiscaleEA, title={Multiscale entropy analysis of biological signals.}, author={Madalena Costa and Ary L. Goldberger and Chung-Kang Peng}, journal={Physical review. E, Statistical, nonlinear, and soft matter physics}, year={2005}, volume={71 2 Pt 1}, pages={ 021906 } }
Traditional approaches to measuring the complexity of biological signals fail to account for the multiple time scales inherent in such time series. These algorithms have yielded contradictory findings when applied to real-world datasets obtained in health and disease states. We describe in detail the basis and implementation of the multiscale entropy (MSE) method. We extend and elaborate previous findings showing its applicability to the fluctuations of the human heartbeat under physiologic and…
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References
SHOWING 1-10 OF 60 REFERENCES
Multiscale entropy analysis of complex physiologic time series.
- MedicinePhysical review letters
- 2002
A method to calculate multiscale entropy (MSE) for complex time series is introduced and it is found that MSE robustly separates healthy and pathologic groups and consistently yields higher values for simulated long-range correlated noise compared to uncorrelated noise.
Multiscale entropy to distinguish physiologic and synthetic RR time series
- Computer ScienceComputers in Cardiology
- 2002
This work addresses the challenge of distinguishing physiologic interbeat interval time series from those generated by synthetic algorithms via a newly developed multiscale entropy method through a first application to a learning set of RR time series derived from healthy subjects.
Assessing Serial Irregularity and Its Implications for Health
- BiologyAnnals of the New York Academy of Sciences
- 2001
The capability of ApEn to assess relatively subtle disruptions, typically found earlier in the history of a subject than mean and variance changes, holds the potential for enhanced preventative and earlier interventionist strategies.
Predicting survival in heart failure case and control subjects by use of fully automated methods for deriving nonlinear and conventional indices of heart rate dynamics.
- MedicineCirculation
- 1997
It is demonstrated that HRV analysis of ambulatory ECG recordings based on fully automated methods can have prognostic value in a population-based study and that nonlinear HRV indices may contribute prognosticvalue to complement traditional HRV measures.
Estimation of the Kolmogorov entropy from a chaotic signal
- Physics
- 1983
While there has been recently a dramatic growth in new mathematical concepts related to chaotic systems, ' the detailed comparison between models and experimental data has lagged somewhat. After…
Application of entropy measures derived from the ergodic theory of dynamical systems to rat locomotor behavior.
- BiologyProceedings of the National Academy of Sciences of the United States of America
- 1990
An important implication of this method is that, in applied ergodic measure-theoretic approaches, the partition that determines the elements of the symbolic dynamical system should not be specified a priori on abstract mathematical grounds but should be chosen relative to its significance with respect to the data set in question.
What is physiologic complexity and how does it change with aging and disease?
- BiologyNeurobiology of Aging
- 2002
Approximate entropy as a measure of system complexity.
- Computer ScienceProceedings of the National Academy of Sciences of the United States of America
- 1991
Analysis of a recently developed family of formulas and statistics, approximate entropy (ApEn), suggests that ApEn can classify complex systems, given at least 1000 data values in diverse settings that include both deterministic chaotic and stochastic processes.
Statistical properties of heartbeat intervals during atrial fibrillation.
- MedicinePhysical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics
- 1996
A theoretical model for the intervals between successive heartbeats during atrial fibrillation based on the following ideas is developed: there is an irregular pattern of activation of the upper chambers of the heart, which is model by a stochastic map.