Adaptive Data Analysis of Complex Fluctuations in physiologic Time Series

We introduce a generic framework of dynamical complexity to understand and quantify fluctuations of physiologic time series. In particular, we discuss the importance of applying adaptive data analysis techniques, such as the empirical mode decomposition algorithm, to address the challenges of nonlinearity and nonstationarity that are typically exhibited in biological fluctuations.

[1]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[2]  C. Peng,et al.  What is physiologic complexity and how does it change with aging and disease? , 2002, Neurobiology of Aging.

[3]  H. Nyquist.  Thermal Agitation of Electric Charge in Conductors , 1928 .

[4]  Madalena Costa,et al.  Multiscale entropy analysis of complex physiologic time series. , 2002, Physical review letters.

[5]  S M Pincus,et al.  Approximate entropy as a measure of system complexity. , 1991, Proceedings of the National Academy of Sciences of the United States of America.

[6]  C. Peng,et al.  Mosaic organization of DNA nucleotides. , 1994, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[7]  Werner Ebeling,et al.  Entropy and complexity of finite sequences as fluctuating quantities. , 2002, Bio Systems.

[8]  Madalena Costa,et al.  Broken asymmetry of the human heartbeat: loss of time irreversibility in aging and disease. , 2005, Physical review letters.

[9]  C. Adami.  What is complexity? , 2002, BioEssays : news and reviews in molecular, cellular and developmental biology.

[10]  C. Peng,et al.  Age-related alterations in the fractal scaling of cardiac interbeat interval dynamics. , 1996, The American journal of physiology.

[11]  H. Stanley,et al.  Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. , 1995, Chaos.

[12]  F. Takens.  Detecting strange attractors in turbulence , 1981 .

[13]  Yaneer Bar-Yam,et al.  Dynamics Of Complex Systems , 2019 .

[14]  B. Pompe,et al.  Permutation entropy: a natural complexity measure for time series. , 2002, Physical review letters.

[15]  Jeffrey M. Hausdorff,et al.  Multiscale entropy analysis of human gait dynamics. , 2003, Physica A.

[16]  Madalena Costa,et al.  Multiscale entropy analysis of biological signals. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[17]  Jeffrey M. Hausdorff,et al.  Fractal dynamics in physiology: Alterations with disease and aging , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[18]  Chung-Kang Peng,et al.  Multimodal pressure-flow method to assess dynamics of cerebral autoregulation in stroke and hypertension , 2004, Biomedical engineering online.

[19]  Jeffrey M. Hausdorff,et al.  Long-range anticorrelations and non-Gaussian behavior of the heartbeat. , 1993, Physical review letters.

[20]  Steven M. Pincus.  Assessing Serial Irregularity and Its Implications for Health , 2001, Annals of the New York Academy of Sciences.

[21]  S. Pincus,et al.  Randomness and degrees of irregularity. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

[22]  Zhaohua Wu,et al.  On the trend, detrending, and variability of nonlinear and nonstationary time series , 2007, Proceedings of the National Academy of Sciences.

[23]  Jeffrey M. Hausdorff,et al.  Altered fractal dynamics of gait: reduced stride-interval correlations with aging and Huntington's disease. , 1997, Journal of applied physiology.

[24]  J. Kurths,et al.  Recurrence-plot-based measures of complexity and their application to heart-rate-variability data. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[25]  J. Richman,et al.  Physiological time-series analysis using approximate entropy and sample entropy. , 2000, American journal of physiology. Heart and circulatory physiology.

[26]  T. Buchman.  The community of the self , 2002, Nature.

[27]  Norden E. Huang,et al.  Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method , 2009, Adv. Data Sci. Adapt. Anal..

[28]  Peter Grassberger,et al.  Entropy estimation of symbol sequences. , 1996, Chaos.

[29]  Jerry Cavallerano,et al.  Altered Phase Interactions between Spontaneous Blood Pressure and Flow Fluctuations in Type 2 Diabetes Mellitus: Nonlinear Assessment of Cerebral Autoregulation. , 2008, Physica A.

[30]  D. Ruelle,et al.  Recurrence Plots of Dynamical Systems , 1987 .

[31]  C. Peng,et al.  Noise and poise: Enhancement of postural complexity in the elderly with a stochastic-resonance–based therapy , 2007, Europhysics letters.

[32]  H. E. Stanley,et al.  Life-support system benefits from noise , 1998, Nature.

[33]  Jeffrey M. Hausdorff,et al.  Quantifying Fractal Dynamics of Human Respiration: Age and Gender Effects , 2002, Annals of Biomedical Engineering.

[34]  Marek Czosnyka,et al.  Nonlinear Assessment of Cerebral Autoregulation from Spontaneous Blood Pressure and Cerebral Blood Flow Fluctuations , 2008, Cardiovascular engineering.

[35]  Chung-Kang Peng,et al.  Broken asymmetry of the human heartbeat: loss of time irreversibility in aging and disease. , 2005 .