1. Time domain summary statistics and frequency domain parameters can be used to measure heart rate variability. More recently, qualitative methods including the Poincaré plot have been used to evaluate heart rate variability. The aim of this study was to validate a novel method of quantitative analysis of the Poincaré plot using conventional statistical techniques.
2. Beat-to-beat heart rate variability was measured over a relatively short period of time (10–20 min) in 12 healthy subjects aged between 20 and 40 years (mean 30 ± 7 years) during (i) supine rest, (ii) head-up tilt (sympathetic activation, parasympathetic nervous system activity withdrawal), (iii) intravenous infusion of atropine (parasympathetic nervous system activity withdrawal), and (iv) after overnight administration of low-dose transdermal scopolamine (parasympathetic nervous system augmentation).
3. The ‘width’ of the Poincaré plot, as quantified by SD delta R—R (the difference between successive R—R intervals), was determined at rest (median 48.9, quartile range 20 ms) and found to be significantly reduced during tilt (median 19.1, quartile range 13.7 ms, P < 0.01) and atropine administration (median 7.1, quartile range 5.7 ms, P < 0.01) and increased by scopolamine (median 79.3, quartile range 33 ms, P < 0.01). Furthermore, log variance of delta R—R intervals correlated almost perfectly with log high-frequency (0.15–0.4 Hz) power (r = 0.99, P < 0.01).
4. These findings strongly suggest that the ‘width’ of the Poincaré plot is a measure of parasympathetic nervous system activity. The Poincaré plot is therefore a quantitative visual tool which can be applied to the analysis of R—R interval data gathered over relatively short time periods.