Nonlinear methods to assess changes in heart rate variability in type 2 diabetic patients.

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