Linear and nonlinear approaches to the analysis of R-R interval variability.
Analysis techniques derived from linear and non-linear dynamics systems theory qualify and quantify physiological signal variability. Both clinicians and researchers use physiological signals in their scopes of practice. The clinician monitors patients with signal-analysis technology, and the researcher analyzes physiological data with signal-analysis techniques. Understanding the theoretical basis for analyzing physiological signals within one's scope of practice ensures proper interpretation of the relationship between physiolgical function and signal variability. This article explains the concepts of linear and nonlinear signal analysis and illustrates these concepts with descriptions of power spectrum analysis and recurrence quantification analysis. This article also briefly describes the relevance of these 2 techniques to R-to-R wave interval (i.e., heart rate variability) signal analysis and demonstrates their application to R-to-R wave interval data obtained from an isolated rat heart model.
Schumacher
Autumn
A
Emory University, Nell Hodgson Woodruff School of Nursing, 1520 Clifton Rd., Suite 305, Atlanta, GA 30322-4207, USA. aschuma@emory.edu
D000818:Animals; D003627:Data Interpretation, Statistical; D004562:Electrocardiography; D006339:Heart Rate; D016014:Linear Models; D023421:Models, Animal; D008955:Models, Cardiovascular; D017711:Nonlinear Dynamics; D051381:Rats; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D013269:Stochastic Processes