Time and Frequency Domain Analysis of Heart Rate Variability and their Correlations in Diabetes Mellitus

Diabetes mellitus (DM) is frequently characterized by autonomic nervous dysfunction. Analysis of heart rate variability (HRV) has become a popular noninvasive tool for assessing the activities of autonomic nervous system (ANS). In this paper, changes in ANS activity are quantified by means of frequency and time domain analysis of R-R interval variability. Electrocardiograms (ECG) of 16 patients suffering from DM and of 16 healthy volunteers were recorded. Frequency domain analysis of extracted normal to normal interval (NN interval) data indicates significant difference in very low frequency (VLF) power, low frequency (LF) power and high frequency (HF) power, between the DM patients and control group. Time domain measures, standard deviation of NN interval (SDNN), root mean square of successive NN interval differences (RMSSD), successive NN intervals differing more than 50 ms (NN50 Count), percentage value of NN50 count (pNN50), HRV triangular index and triangular interpolation of NN intervals (TINN) also show significant difference between the DM patients and control group. Keywords—Autonomic nervous system, diabetes mellitus, frequency domain and time domain analysis, heart rate variability.

[1]  S. Colagiuri,et al.  The Diabetes Control and Complications Trial , 1983, Henry Ford Hospital medical journal.

[2]  A.E. Aubert,et al.  Analysis of heart rate variability using power spectral analysis and nonlinear dynamics , 1994, Computers in Cardiology 1994.

[3]  The Effect of Intensive Diabetes Therapy on the Development and Progression of Neuropathy , 1995, Annals of Internal Medicine.

[4]  A. Malliani,et al.  Heart rate variability. Standards of measurement, physiological interpretation, and clinical use , 1996 .

[5]  W. Tompkins,et al.  Time domain based algorithm for detection of ventricular fibrillation , 1997, Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 'Magnificent Milestones and Emerging Opportunities in Medical Engineering' (Cat. No.97CH36136).

[6]  A. Uehara,et al.  Diabetic cardiac autonomic dysfunction: Parasympathetic versus sympathetic , 1999, Annals of nuclear medicine.

[7]  J. Goldberger,et al.  Sympathovagal balance: how should we measure it? , 1999, The American journal of physiology.

[8]  M. Møller,et al.  Parasympathetic function during deep breathing in the general population: relation to coronary risk factors and normal range , 1999, Journal of internal medicine.

[9]  M. Curione,et al.  Is a Reduced Entropy in Heart Rate Variability an Early Finding of Silent Cardiac Neurovegetative Dysautonomia in Type 2 Diabetes Mellitus , 2001 .

[10]  A. Al-hazimi,et al.  Time-domain analysis of heart rate variability in diabetic patients with and without autonomic neuropathy. , 2002, Annals of Saudi medicine.

[11]  H. Jelinek Automated assessment of cardiovascular disease associated with diabetes in rural and remote health care practice , 2004 .

[12]  J. Sztajzel Heart rate variability: a noninvasive electrocardiographic method to measure the autonomic nervous system. , 2004, Swiss medical weekly.

[13]  L. Chambless,et al.  Diabetes, glucose, insulin, and heart rate variability: the Atherosclerosis Risk in Communities (ARIC) study. , 2005, Diabetes care.

[14]  B. Sumpio,et al.  Review of prevalence and outcome of vascular disease in patients with diabetes mellitus. , 2006, European journal of vascular and endovascular surgery : the official journal of the European Society for Vascular Surgery.

[15]  M. Fisher,et al.  Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) , 2007 .