A Computational Model of Autonomic Nervous System for Heart Rate Variability

Heart Rate Variability (HRV) is the subtle beat to beat changes in heart rate. Autonomic Nervous System (ANS) regulates heart rate by controlling the neurotransmitters, mainly Norepinephrine (NE) and Acetyl choline (Ach) from sympathetic and parasympathetic branches respectively. HRV analysis is a noninvasive tool for assessing the integrity of ANS. HRV changes are observed in the onset of heart disease and in a number of disease conditions like sleep apnea, psychiatric disorders, diabetes, hypertension etc. An understanding of the relationship between kinetics at sympathetic and parasympathetic sites and HRV helps to identify biological changes associated with various autonomic imbalance conditions and hence help in targeted diagnosis and therapy. A computational model of ANS for heart rate regulation is proposed in this study. Fitzhugh Nagumo (FHN) model is used as the successive stage of proposed model to generate a discrete time heart beat interval series. HRV data from a group of healthy individuals having balanced sympathetic and parasympathetic activities were studied. The results were in agreement with parameters derived from model synthesized data for the same autonomic state.