More than 50 years ago, during the investigation of fetal distress, it was realized that the variation of the heart rate, i. e., Heart Rate Variability (HRV), is a marker of the health status, since it unveils changes in beat-to-beat variation of the heart, even before there was a remarkable change in heart rate itself. HRV reflects the balance between the sympathetic and the parasympathetic nervous system. Furthermore, several physiological effects, which influence the normal rhythm of the heart, are manifested by HRV . Since the heart rate itself is nonstationary and the structure generating the signal involves nonlinear contributions, nonlinear methods to quantify the variability of the heart rate gained interest over the last years. Cardiovascular Diseases (CVD) are more common and their occurrences are increasing since centuries. HRV analysis is a useful noninvasive tool for early detection and the prevention of CVD. The goal of this thesis is to implement numerous indices to quantify HRV derived from mathematical models and compare them to each other in different test cases. Most of them belong to the section of nonlinear methods, though some other standard measures, as statistical parameters and one index of the time-frequency domain, are calculated. The implemented methods are tested on their ability to differentiate between healthy and pathological subjects. Furthermore, their sensitivity to a varying data length is investigated. In addition, the HRV measures are tested if there is a difference between young and elderly people. The last test case examines subjects with ventricular arrhythmias. The models are applied to baseline data and on-therapy data, i. e., during medication, of the same subject in order to detect effects of antiarrhythmic treatment. The results show that all of the implemented indices are able to differ entiate between nonpathological and pathological subjects. Most of them show a significant difference before and after antiarrhythmic treatment, though no index is sensitive to age. Their robustness to varying lengths of recordings is formidable. The well-trodden statistical indices justified their existence with signif icant differences in all, except the age-dependency, test cases. However, they are strongly correlated to each other. Apart from the age-dependency test case, all of the fractal indices show thoroughly remarkable results, too. Just one of them found no significant differences before and after antarrhythmic treatment. Two of them are independent for all the applied data. In summary, this thesis shows that fractal descriptors are an appropriate support for analyzing the HRV , and therefore to prevent or detect cardiovascular diseases. Especially the Hurst exponent, well established in the financial community, should get more attention in analyzing biomedical signals, such as HRV.