The traditional indexes of heart rate (HR) variability may lack the ability to detect subtle, but important changes in HR behavior. Nonlinear heart rate variability (HRV) analysis methods that are based on chaos theory can reveal subtle abnormalities in the HR dynamics of patients with cardiovascular diseases. Therefore, we tested the validity of nonlinear analysis methods as markers to differentiate normal and abnormal HR dynamics in the cardiovascular disease state.

One-hundred patients were studied: 70 patients with left ventricular dysfunction (LVD), including 40 post-myocardial infarct patients (PMI) and 30 dilated cardiomyopahty patients (DCM), and 30 age and gender-matched controls. One-hour, 6-hours (day and night each) and 24 hours of R-R interval data from 24-hour Holter recordings were subjected to the conventional time and frequency-domain analysis. The ApEn, short-term (α₁) and long-term (α₂) scaling exponents of the detrended fluctuation analysis (DFA) and the power-law exponent (β) were also measured.

Conventional linear measures did not show a significant difference except for the VLF, lnLF and the LF/HF ratio between the controls and the LVD patients. Among the analyzed parameters, β, β₂ and α₁ were the most powerful discriminators. The β of the normal and LVD patients was -1.10±0.29 and -0.70±0.40, respectively (p<0.001), and the α₁ was 1.08±0.23 and 0.81±0.28, respectively (p<0.001). The β, β₂ and α₁ can discriminate the etiologic cause of LVD. The length of the R-R interval data did not affect the result, and a significant correlation was observed. The individual values of the fractal and complexity measures were more stable than those of the conventional linear measures.

We conclude that the measures derived from fractal and complexity methods are useful for detecting altered HR dynamics of LVD and for improving the shortcomings of the conventional measures.