Multiscale entropy analysis of biological signals.

  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},
  volume={71 2 Pt 1},
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… 

Classification of heart rate signals of healthy and pathological subjects using threshold based symbolic entropy.

The findings indicated that there is reduction in the complexity of pathological subjects as compared to healthy ones at wide range of threshold values and demonstrated that complexity decreased with disease severity.

Studying the dynamics of interbeat interval time series of healthy and congestive heart failure subjects using scale based symbolic entropy analysis

This study has proposed multiscale normalized corrected Shannon entropy (MNCSE), in which instead of using sample entropy, symbolic entropy measure NCSE has been used as an entropy estimate, and the preliminary results indicate that MNCSE values are more stable and reliable than original MSE values.

Suitability of multiscale entropy for complexity quantification of cardiac rhythms in chronic pathological conditions: a similarity patterns based investigation

It is concluded that MSE measure both the entropy and short term variations associated with a time series, but unable to investigate the real complexity (meaning full structural organization) present in a signal.

Multiscale entropy analysis of biological signals: a fundamental bi-scaling law

A fundamental bi-scaling law for fractal time series is derived, one for the scale in phase space, the other for the block size used for smoothing, which illustrates the usefulness of the approach by examining two types of physiological data.

Cardiac variability time-series analysis by sample entropy and multiscale entropy

It is demonstrated that SampEn is small for higher values of tolerance r and is able to distinguish different physiologic time series with tolerance level r, and this traditional algorithm exhibits higher complexity for pathologic subjects than for healthy physiologic subjects, which is misleading observation.

A complexity measure for heart rate signals

Power-Law Exponent Modulated Multiscale Entropy: A Complexity Measure Applied to Physiologic Time Series

This paper proposed a new method, namely the power-law exponent modulated multiscale entropy (pMSE), as a complexity measure for physiologic time series and demonstrated that it could solve the above two difficulties of the MSE method.

Extraction of Dynamical Information and Classification of Heart Rate Variability Signals Using Scale Based Permutation Entropy Measures

The results revealed that MPE and IMPE resolved the issue of inducing undefined entropy estimates and are robust in classifying healthy and different pathological subjects and to classify HRV signals under different physiological and pathological conditions.



Multiscale entropy analysis of complex physiologic time series.

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

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

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.

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

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.

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.

Approximate entropy as a measure of system complexity.

  • Steven M. Pincus
  • Computer Science
    Proceedings 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.

  • ZengGlass
  • Medicine
    Physical 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.