Cardiovascular signal processing; Cardiovascular Image processing; Cardiovascular Modelling; Artificial intelligence; Digital cardiology
Cardiovascular Bioengineering mainly focuses on applying engineering techniques such as traditional as well as AI-based signal and images processing and physiologic system modelling to the cardiovascular system. We design and develop new algorithms finalized to: provide computer-based support systems to identify patients affected by cardiac diseases (such as myocardial infarction, Brugada Syndrome, Long QT syndrome, ect) or arrhythmias (such as atrial fibrillation ) from electrocardiographic tracings; define and quantify non-invasive indexes of cardiac risk (such as T-wave alternans and QT interval); monitor cardiac health status in subjects in specific conditions (such as pregnancy; physical activity and mental stress); reconstruct identify and segment cardiac anatomical structures (such as atria and ventricles) from images; and assess associations between cardiac diseases and health problems related to other physiological systems.
University of Leiden (The Netherlands), University of Rochester (USA), Universidad de Zaragoza (Spain); Czech Technical University (Repubblica Ceca); Jagiellonian University Medical College (Poland); Lund University (Sweden); Universitas Sriwijaya (Indonesia); Università degli Studi di Roma “Foro Italico; Politecnico di Torino; Politecnico di Milano; Università degli Studi di Milano; Università degli Studi “G. d’Annunzio”(Chieti/Pescara); Ospedali Riuniti di Ancona; INRCA Ancona; Gruppo San Donato, Milano; UNIVPM (DICEA).
