title: Emergence of dynamical complexity related to human heart rate variability.
abstract: We apply the refined composite multiscale entropy (MSE) method to a one-dimensional directed small-world network composed of nodes whose states are binary and whose dynamics obey the majority rule. We find that the resulting fluctuating signal becomes dynamically complex. This dynamical complexity is caused (i) by the presence of both short-range connections and long-range shortcuts and (ii) by how well the system can adapt to the noisy environment. By tuning the adaptability of the environment and the long-range shortcuts we can increase or decrease the dynamical complexity, thereby modeling trends found in the MSE of a healthy human heart rate in different physiological states. When the shortcut and adaptability values increase, the complexity in the system dynamics becomes uncorrelated.
authors.affiliation: Center for Dynamical Biomarkers and Translational Medicine, National Central University, Jhongli 32001, Taiwan and Center for Polymer Studies and Department of Physics, Boston University, Boston, Massachusetts 02215, USA.
authors.forename: Mei-Chu
authors.initials: MC
authors.lastname: Chang
authors.affiliation: Cardiovascular Division and Margret and H. A. Rey Institute for Nonlinear Dynamics in Medicine, Beth Israel Deaconess Medical Center/Harvard Medical School, Boston, Massachusetts 02215, USA.
authors.forename: C-K
authors.initials: CK
authors.lastname: Peng
authors.affiliation: Center for Polymer Studies and Department of Physics, Boston University, Boston, Massachusetts 02215, USA.
authors.forename: H Eugene
authors.initials: HE
authors.lastname: Stanley
mesh_terms: D000328:Adult; D000368:Aged; D000375:Aging / Q000502:physiology; D006339:Heart Rate; D006801:Humans; D008297:Male; D008955:Models, Cardiovascular; D055815:Young Adult