Linear and Nonlinear Heart Rate Variability Indexes in Clinical Practice

Biological organisms have intrinsic control systems that act in response to internal and external stimuli maintaining homeostasis. Human heart rate is not regular and varies in time and such variability, also known as heart rate variability (HRV), is not random. HRV depends upon organism's physiologic and/or pathologic state. Physicians are always interested in predicting patient's risk of developing major and life-threatening complications. Understanding biological signals behavior helps to characterize patient's state and might represent a step toward a better care. The main advantage of signals such as HRV indexes is that it can be calculated in real time in noninvasive manner, while all current biomarkers used in clinical practice are discrete and imply blood sample analysis. In this paper HRV linear and nonlinear indexes are reviewed and data from real patients are provided to show how these indexes might be used in clinical practice.

[1]  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.

[2]  Benoit B. Mandelbrot,et al.  Fractal Geometry of Nature , 1984 .

[3]  K Sroka,et al.  Heart rate variability in myocardial ischemia during daily life. , 1997, Journal of electrocardiology.

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

[5]  G. Breithardt,et al.  Heart rate variability: standards of measurement, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. , 1996 .

[6]  C. Peng,et al.  Cardiac interbeat interval dynamics from childhood to senescence : comparison of conventional and new measures based on fractals and chaos theory. , 1999, Circulation.

[7]  A. Seely,et al.  Multiple organ dysfunction syndrome: Exploring the paradigm of complex nonlinear systems , 2000, Critical care medicine.

[8]  A. Malliani,et al.  Heart rate variability. Standards of measurement, physiological interpretation, and clinical use , 1996 .

[9]  F. Basile,et al.  Heart rate variability and myocardial infarction: systematic literature review and metanalysis. , 2009, European review for medical and pharmacological sciences.

[10]  H. Huikuri,et al.  Fractal analysis of heart rate variability and mortality after an acute myocardial infarction. , 2002, The American journal of cardiology.

[11]  P. Macklem,et al.  Homeokinesis and short-term variability of human airway caliber. , 2001, Journal of applied physiology.

[12]  P. Macklem,et al.  Complex systems and the technology of variability analysis , 2004, Critical Care.

[13]  T. Gisiger.  Scale invariance in biology: coincidence or footprint of a universal mechanism? , 2001, Biological reviews of the Cambridge Philosophical Society.

[14]  K. Newell,et al.  Changing complexity in human behavior and physiology through aging and disease , 2002, Neurobiology of Aging.

[15]  Naoyuki Hirata,et al.  Differential Effects of Propofol and Sevoflurane on Heart Rate Variability , 2003, Anesthesiology.

[16]  A. Goldberger.  Fractal Variability Versus Pathologic Periodicity: Complexity Loss and Stereotypy in Disease , 1997, Perspectives in biology and medicine.

[17]  Atul J Butte,et al.  Identification of complex metabolic states in critically injured patients using bioinformatic cluster analysis , 2010, Critical care.

[18]  Tim Appenzeller,et al.  Beyond Reductionism , 1999, Science.

[19]  H. Huikuri,et al.  Heart rate variability in ischemic heart disease , 2001, Autonomic Neuroscience.

[20]  Masahiro Umino,et al.  The Different Effects of Intravenous Propofol and Midazolam Sedation on Hemodynamic and Heart Rate Variability , 2005, Anesthesia and analgesia.