Multi-parametric prediction for cardiovascular risk assessment

摘要

The employment of personal health systems (pHealth) is a valuable concept in the management of chronic diseases, particularly in the context of cardiovascular diseases. By means of a continuous monitoring of the patient it is possible to seamless access multiple sources of data, including physiological signals, providing professionals with a global and reliable view of the patient's status. In practice, it is possible the prompt diagnosis of events, the early prediction of critical events and the implementation of personalized therapies. Furthermore, the information collected during long periods creates new opportunities in the diagnosis of a disease, in its evolution, and in the prediction of possible complications. The focus of this work is the research and implementation of multi-parametric algorithms for data analysis in pHealth context, including data mining techniques as well as physiological signal modelling and processing. In particular, fusion strategies for cardiovascular status evaluation (namely cardiovascular risk assessment and cardiac function estimation) and multi-parametric prediction algorithms for the early detection of cardiovascular events (such as hypertension, syncope and heart failure decompensation) will be addressed.