Biomaterial Database

Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. 1995

C K Peng, and S Havlin, and H E Stanley, and A L Goldberger

Cardiovascular Division, Harvard Medical School, Boston, Massachusetts 02215, USA.

The healthy heartbeat is traditionally thought to be regulated according to the classical principle of homeostasis whereby physiologic systems operate to reduce variability and achieve an equilibrium-like state [Physiol. Rev. 9, 399-431 (1929)]. However, recent studies [Phys. Rev. Lett. 70, 1343-1346 (1993); Fractals in Biology and Medicine (Birkhauser-Verlag, Basel, 1994), pp. 55-65] reveal that under normal conditions, beat-to-beat fluctuations in heart rate display the kind of long-range correlations typically exhibited by dynamical systems far from equilibrium [Phys. Rev. Lett. 59, 381-384 (1987)]. In contrast, heart rate time series from patients with severe congestive heart failure show a breakdown of this long-range correlation behavior. We describe a new method--detrended fluctuation analysis (DFA)--for quantifying this correlation property in non-stationary physiological time series. Application of this technique shows evidence for a crossover phenomenon associated with a change in short and long-range scaling exponents. This method may be of use in distinguishing healthy from pathologic data sets based on differences in these scaling properties.

UI	MeSH Term	Description	Entries
D008433	Mathematics	The deductive study of shape, quantity, and dependence. (From McGraw-Hill Dictionary of Scientific and Technical Terms, 6th ed)	Mathematic
D006331	Heart Diseases	Pathological conditions involving the HEART including its structural and functional abnormalities.	Cardiac Disorders,Heart Disorders,Cardiac Diseases,Cardiac Disease,Cardiac Disorder,Heart Disease,Heart Disorder
D006333	Heart Failure	A heterogeneous condition in which the heart is unable to pump out sufficient blood to meet the metabolic need of the body. Heart failure can be caused by structural defects, functional abnormalities (VENTRICULAR DYSFUNCTION), or a sudden overload beyond its capacity. Chronic heart failure is more common than acute heart failure which results from sudden insult to cardiac function, such as MYOCARDIAL INFARCTION.	Cardiac Failure,Heart Decompensation,Congestive Heart Failure,Heart Failure, Congestive,Heart Failure, Left-Sided,Heart Failure, Right-Sided,Left-Sided Heart Failure,Myocardial Failure,Right-Sided Heart Failure,Decompensation, Heart,Heart Failure, Left Sided,Heart Failure, Right Sided,Left Sided Heart Failure,Right Sided Heart Failure
D006339	Heart Rate	The number of times the HEART VENTRICLES contract per unit of time, usually per minute.	Cardiac Rate,Chronotropism, Cardiac,Heart Rate Control,Heartbeat,Pulse Rate,Cardiac Chronotropy,Cardiac Chronotropism,Cardiac Rates,Chronotropy, Cardiac,Control, Heart Rate,Heart Rates,Heartbeats,Pulse Rates,Rate Control, Heart,Rate, Cardiac,Rate, Heart,Rate, Pulse
D006801	Humans	Members of the species Homo sapiens.	Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D013269	Stochastic Processes	Processes that incorporate some element of randomness, used particularly to refer to a time series of random variables.	Process, Stochastic,Stochastic Process,Processes, Stochastic
D013599	Systole	Period of contraction of the HEART, especially of the HEART VENTRICLES.	Systolic Time Interval,Interval, Systolic Time,Intervals, Systolic Time,Systoles,Systolic Time Intervals,Time Interval, Systolic,Time Intervals, Systolic
D013997	Time Factors	Elements of limited time intervals, contributing to particular results or situations.	Time Series,Factor, Time,Time Factor
D015233	Models, Statistical	Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc.	Probabilistic Models,Statistical Models,Two-Parameter Models,Model, Statistical,Models, Binomial,Models, Polynomial,Statistical Model,Binomial Model,Binomial Models,Model, Binomial,Model, Polynomial,Model, Probabilistic,Model, Two-Parameter,Models, Probabilistic,Models, Two-Parameter,Polynomial Model,Polynomial Models,Probabilistic Model,Two Parameter Models,Two-Parameter Model
D017711	Nonlinear Dynamics	The study of systems which respond disproportionately (nonlinearly) to initial conditions or perturbing stimuli. Nonlinear systems may exhibit "chaos" which is classically characterized as sensitive dependence on initial conditions. Chaotic systems, while distinguished from more ordered periodic systems, are not random. When their behavior over time is appropriately displayed (in "phase space"), constraints are evident which are described by "strange attractors". Phase space representations of chaotic systems, or strange attractors, usually reveal fractal (FRACTALS) self-similarity across time scales. Natural, including biological, systems often display nonlinear dynamics and chaos.	Chaos Theory,Models, Nonlinear,Non-linear Dynamics,Non-linear Models,Chaos Theories,Dynamics, Non-linear,Dynamics, Nonlinear,Model, Non-linear,Model, Nonlinear,Models, Non-linear,Non linear Dynamics,Non linear Models,Non-linear Dynamic,Non-linear Model,Nonlinear Dynamic,Nonlinear Model,Nonlinear Models,Theories, Chaos,Theory, Chaos