Nowadays, mouse models and the analysis of murine biosignals are increasingly employed for the study and the comprehension of cardiovascular diseases (CVD). This master thesis involves the implementation and testing of algorithms in Matlab for the analysis of electrocardiograms (ECG) and pulse waves (PW), and its application to mice. Emphasis is put on approaches already used in cardiovascular research and their implementation. In a first step, an analysis and a review of the state of the art in mouse models in cardiovascular research has been carried out, focusing on ECG and pulse wave recordings. Approaches have been selected jointly based on literature review and available data. Thereafter, algorithms have been implemented in order to automatically calculate different cardiovascular parameters. Furthermore, algorithms have been evaluated and applied to different groups of mice. Four datasets of mice containing physiological and pulse wave information have been provided by the clinical partner from the University of Pisa, Department of Clinical and Experimental Medicine. This data cover 13 wild type male mice at the age of 8 and 25 weeks, 10 ApoE mice 16 weeks old, and 9 diabetes mice 13 weeks old. Mice were examined with a non-invasive high resolution ultrasound imaging system. Implemented algorithms allow to apply four different analysis in order to compare cardiovascular parameters between two groups of mice. These analyses are: (1) a physiological comparison, (2) ECG intervals comparison, (3) wave intensity analysis (WIA) and (4) vascular analysis. Three different test cases (TC) have been analyzed to test changes due to age (TC1), to study the influence of carotid plaque vulnerability (TC2) and to evaluate differences in various parameters caused by diabetes (TC3). Each test case consists of two different groups of mice. TC1 has been performed between young and old wild type (WT) mice. In TC2, young wild type and atherosclerosis mice have been compared. Lastly, TC3 has been performed between young wild type and diabetes mice. Exemplarily, in TC1, parameters like W1 or reID are significantly higher in young WT mice than in old WT mice. On the contrary, AIx is higher in old WT mice. These results show that aging causes a reduction in the cardiac performance, indicated by the decrease in magnitude of the forward wave (W1) and the increase of the influence of reflected waves (AIx). Besides, the low distension capability of the artery (reID) in old WT mice shows that the carotid artery dilates with age. Furthermore, although pulse wave velocity (PWV) is similar between young and old WT mice, it increases in mice with atherosclerosis and diabetes. Thus, a connection between arterial stiffening and diabetes is most likely. This behaviour can as well be observed in humans. In conclusion, this thesis presents an algorithm for the automatic identification of different cardiovascular parameters using murine signals achieved by non-invasive techniques. Despite some limitations, like a small sample size, results are promising and allow further investigations.