• Autumn M Schumacher, Joseph P Zbilut, Charles L Webber, Dorie W Schwertz, and Mariann R Piano.
• Department of Physiological and Technological Nursing, School of Nursing at the Medical College of Georgia, Augusta, 30912, USA. aschumacher@mcg.edu
• Biol Res Nurs. 2006 Jul 1; 8 (1): 55-66.

AbstractCardiac variability can be assessed from two perspectives: beat-to-beat performance and continuous performance during the cardiac cycle. Linear analysis techniques assess cardiac variability by measuring the physical attributes of a signal, whereas nonlinear techniques evaluate signal dynamics. This study sought to determine if recurrence quantification analysis (RQA), a nonlinear technique, could detect pharmacologically induced autonomic changes in the continuous left ventricular pressure (LVP) and electrographic (EC) signals from an isolated rat heart-a model that theoretically contains no inherent variability. LVP and EC signal data were acquired simultaneously during Langendorff perfusion of isolated rat hearts before and after the addition of acetylcholine (n = 11), norepinephrine (n = 12), or no drug (n = 12). Two-minute segments of the continuous LVP and EC signal data were analyzed by RQA. Findings showed that%recurrence,%determinism, entropy, maxline, and trend from the continuous LVP signal significantly increased in the presence of both acetylcholine and norepinephrine, although systolic LVP significantly increased only with norepinephrine. In the continuous EC signal, the RQA trend variable significantly increased in the presence of norepinephrine. These results suggest that when either the sympathetic or parasympathetic division of the autonomic nervous system overwhelms the other, the dynamics underlying cardiac variability become stationary. This study also shows that information concerning inherent variability in the isolated rat heart can be gained via RQA of the continuous cardiac signal. Although speculative, RQA may be a tool for detecting alterations in cardiac variability and evaluating signal dynamics as a nonlinear indicator of cardiac pathology.

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