Silvia Caligari

Assegnista di Ricerca
Dipartimento di Elettronica e Telecomunicazioni (DET)


Altre attività e progetti di ricerca

Risk stratification in Brugada syndrome through AI-enhanced electrocardiograms

Brugada Syndrome is a potentially life-threatening arrhythmic disorder that determines an increased probability to develop arrhythmic events in young and otherwise individuals. Cardiac arrest is often the first clinical manifestation of the disease. A correct evaluation of the risk of developing an arrhythmic event could prevent premature deaths and unnecessary procedures. The idea is that Machine Learning analysis can retrieve complex information and correctly predict whether a patient will develop an event or not.

International PhD program in Computational Mathematics and Decision Sciences

The Thesis has been focused on an EFSI model for cardiac tissues based on a fluid-structure interaction framework inspired by the Immersed Boundary Method for predicting the interaction of complex structures immersed in laminar, transitional and turbulent flows. The key elements of these methods are: a high-order Navier-Stokes solver, an ??2-projection approach for coupling structure and fluid solver and the solution of the elastodynamics equations.

Starting from the FSI problem, a time dependent active term with a delay given by the solution of an eikonal equations, is added to the solid behavior, as a first attempt to represent an active contribution due to an external force or a muscle contraction in a biological framework.