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[Nonlinear dynamic analysis of heart rate variability in patients with diabetic autonomic neuropathy]
Yong-qin Li1, Fang Gao, Qi Geng
1Laboratory of Medical Physics, Department of Biomedical Engineering, First Military Medical University, Guangzhou 510515, China. ken@fimmu.com
Objective:
To explore earlier detection methods for diabetic autonomic neuropathy (DAN) by heart rate variability (HRV) analysis.
Methods:
Thirty-four diabetic patients (including 22 with explicit clinical DAN symptoms) were randomly selected for this study from the in-patient department of endocrinology. On the basis of Virtual Instrumental WorkBench- LabVIEW and using several nonlinear dynamic analysis measures, including Allan factor, lyapunov exponent, approximate entropy, fractal dimension, complexity, wavelet-transform standard deviation and nonlinear energy operator, the analysis of the HRV in these diabetic patients was performed in comparison with normal subjects.
Results:
The nonlinear indices of both DAN patients and patients without obvious DAN were significantly different from those of the normal subjects, especially in terms of Lyapunov exponent, approximate entropy, and nonlinear energy operator.
Conclusion:
Nonlinear dynamic methods of HRV analysis can provide assistance in assessing the status and impairment of the autonomic system, and can be used to efficiently detect diabetic neuropathy in early stages.