BACKGROUND: Heart rate variability (HRV) is an important indicator of autonomic modulation of cardiovascular function. Diabetes can alter cardiac autonomic modulation by damaging afferent inputs, thereby increasing the risk of cardiovascular disease. We applied nonlinear analytical methods to identify parameters associated with HRV that are indicative of changes in autonomic modulation of heart function in diabetic patients. OBJECTIVE: We analyzed differences in HRV patterns between diabetic and age-matched healthy control subjects using nonlinear methods. METHODS: Methods: Lagged Poincaré plot, autocorrelation, and detrended fluctuation analysis were applied to analyze HRV in electrocardiography (ECG) recordings. RESULTS: Lagged Poincaré plot analysis revealed a decrease in the standard deviation of instantaneous beat-to-beat interval variability (SD1) and in the ratio of SD1 to the continuous long-term R-R interval variability (SD12) in the diabetic group, indicating a decrease in heart rate parasympathetic modulation. The detrended fluctuation exponent derived from long-term fitting was higher than the short-term one in the diabetic population, which was also consistent with decreased parasympathetic input. The autocorrelation function of the deviation of inter-beat intervals exhibited a highly correlated pattern in the diabetic group compared with the control group. CONCLUSIONS: The HRV pattern significantly differs between diabetic patients and healthy subjects. All three statistical methods employed in the study may prove useful to detect the onset and extent of autonomic neuropathy in diabetic patients.
Heart Failure; Diabetes Mellitus; Type 1; Systole; Measurements, Methods and Theories; Statistics as Topic
