Background: Recently nonlinear analysis of heart rate (HR) dynamics has been introduced to reveal the hidden information in various cardiac abnormalities. The heart rate in the healthy exhibit fractal behavior, and long-range (fractal) correlations break down in disease and aging. We applied these nonlinear methods to understand the HR dynamics in sinus node dysfunction (SND) and tested the feasibility as a noninvasive method for the detection of SND. Methods: Corrected SN recovery time (cSNRT) was determined in all patients. Fractal scaling exponents (慣2, 慣2), ApEn or SampEn as a complexity measure, and CSE, as well as the traditional time- and frequency-domain analysis methods were used. One-hundred eight 60-min period RR interval data excerpted from 24-h ambulatory ECG of 27 SND patients and 27 sex and age-matching controls. Results: cSNRT was longer in SND (448짹326, P=0.003). Mean R-R interval, SDNN and pNN50 were significantly different between the 2 groups. Low-frequency (LF) and high frequency (HF) power did not differ. The short-term (���11 beats) scaling exponent (慣1), ApEn and SampEn did not differ between the 2 groups except CSE50 (1.38짹1.02 vs. 0.57짹0.64 of control, P=0.003). However, long-term scaling exponent (慣2) was reduced in SND (1.04짹0.17 vs. 1.12짹0.13 of control, P=0.017), and remained as a most statistically significant parameter in univariate analysis. When analyzed after dividing into day-time and night-time episode, the difference of 慣2 between groups was still significant irrespective of sampling time. There was no significant correlation between cSNRT and 慣2. Conclusion: Altered HR dynamics was found in SND. Long-term fractal scaling exponent, 慣2, might be an independent and new noninvasive tool for the detection of SND.