Could Non-Linear Heart Rate Variability Analysis of Short RR Intervals Series Give Clinically Valuable Information in Heart Disease?

New analytic methods based on nonlinear system theory have been developed to characterize the nonlinear features in HR dynamics. It is known from long time series (24h ECG recordings) that patients with chronic heart failure or stable coronary heart disease have altered fractal organization in heartbeat dynamics. During such long-time series, many confounding could limit the assessment of autonomic functions. The aim of this study was to test the hypothesis that non-linear indices obtained from short RR intervals series (256 points) can reveal abnormalities in HR behavior in cardiac disease. In 18 healthy subjects, 42 coronary artery disease and 32 chronic heart failure patients, heart rate variability was characterized during supine rest and active standing by spectral analysis, the short- and long-term fluctuations in the R-R interval, approximate and sample entropy and correlation dimension.

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