Abstract

According to the World Health Organisation, diseases of the cardiovascular system (CVS) are currently the main cause of death all over the world. Therefore, their understanding, prediction, and prevention with the help of non-invasive, cost effective methods is of great interest. Analysis of the heart rate and its change over time can give valuable insight into the health status of a patient, and is easily derived from electrocardiogram (ECG) data. Reduced heart rate variability (HRV) is associated to an increased probability of dying after myocardial infarctions and indicates inflammatory processes. It is symptomatic of mental disorders such as depression and burn-out. Different approaches in modeling and simulation of HRV can provide new insight into the nonlinear interplay of cardiovascular regulation. In this work, three models for HRV are implemented and compared. They include the firing rate of the baroreceptors, respiration, activity of the sympathetic and parasympathetic nervous system, stroke volume, cardiac noradrenaline and acetylcholine concentration, as well as a windkessel model including peripheral resistance and arterial compliance. First, an existing model for HRV based on respiration and baroreflex activity was implemented and analyzed. A second model was created through adaption of the first model. Based on a model for the autonomic response to orthostatic stress, a third model was implemented as well. All models were realized in Simulink 2017b, and their validation is performed based on 60 five-minute ECG recordings from 30 subjects. The simulation results are compared to subject data based on the standards of HRV measurement by the Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Each of the three modeling approaches showed specific advantages, disadvantages, and possibilities for further improvement. The results provide basis for extension of HRV models, paving the way for the future usage of model prediction in the field of cardiovascular diseases.