Assessment of Autonomic Function for Healthy and Diabetic Patients Using Entrainment Methods and Spectral Technique

Abstract

The main objective of this paper is to assess the autonomic function of healthy subjects and diabetic patients by using autopower spectra and measurement of gain frequency response between breathing signals at different rates as input and heart rate variability signals (HRV) as output.Twenty healthy subjects (39± 3.6) years old and twenty one diabetic patients (41±2.4) years old participated in this study . The subject lies on a bed where Electrocardiogram (ECG) , heart rate variability signals (HRV) extracted from ECG and respiration signals at different breathing rates were measured and recorded. The autopower spectra of breathing rates and HRV signals were produced using the auto-regressive method. The gain frequency response for both healthy and diabetic groups was obtained . The effect of respiration signals at different rates on the auto-power of HRV signals significant specially for healthy subjects on a rate of 6 cycle /min (6 c pm or 0.1 Hz). However, this effect is reduced in all diabetic patients examined. The gain response of healthy group shows high gain with two peaks at 0.1 Hz and 0.2 Hz and high standard deviation (SD).

Epileptic Seizure: A New Approach for Quantification of Autonomic Deregulation with Chaos Based Technique

“…The finding of Kolsal et al [24] is interesting but the technique that has been used for analysis is the conventional linear technique which has been challenged for quite some time for non-stationary signals. Any signal, the spectrum may cover wide range of frequencies and conventional time and frequency domain analysis techniques based on the linear fluctuation of heart rate is insufficient to outline the changes in heart rate dynamics [25][26][27][28][29][30][31][32][33][34][35][36]. To quantify this, nonlinear dynamics based methods such as fractal analysis and chaos theory have been introduced [37][38][39].…”

Novel Method for Assessment of Autonomic Function in Health and Disease: An Application to Epilepsy

“…However, theories propounded on the mechanism of sudden death have concentrated on autonomic dysfunction and have included cardiac arrhythmia and apnea [8]. Conventional time and frequency domain analysis techniques based on the linear fluctuation of heart rate insufficient in outline the changes in heart rate dynamics [12][13][14][15][16][17][18][19][20][21][22][23], therefore, new methods based on nonlinear dynamics have been introduced to quantify complex heart rate dynamics and complement conventional measures of its variability. One aim of this study is to propose another approach using Volterra kernel for system identification of nonlinear relationship between input stimulus (lowering and raising leg) and the output (HRV signals) to assess the autonomic function of healthy subjects and Epileptic patients and provide insight into the autonomic dysfunction of Epilepsy patients compared with healthy subjects.…”