According to the World Health Organisation, diseases of the cardiovascular system (CVS) are currently the main cause of death in high-, middle-, and low-income countries. Therefore, their understanding, prediction, and prevention with the help of non-invasive, cost effective, and quick 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 data. Reduced heart rate variability (HRV) is associated to an increased probability of dying after myocardial infarctions and indicates inflammatory processes in the body. It is symptomatic of mental disorders such as depression and even serves as an indicator for the risk of suffering from 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. For this purpose, sympathetic activity, as well as the pressure curve in the aortic arch and the duration of the systole were adapted. Based on a model for the autonomic response to orthostatic stress, a third model, including three different types of baroreceptors and a dependence on the mean arterial pressure, was implemented as well. All three models were realized in Simulink 2017b, and their validation is performed based on two 5 minute electrocardiogram (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. Lastly, the results once more underline the complex and nonlinear modulation of HRV, and provide basis for extension of HRV models, paving the way for the future usage of model prediction in the field of cardiovascular diseases.