logo SBA

ETD

Archivio digitale delle tesi discusse presso l’Università di Pisa

Tesi etd-12292023-153451


Tipo di tesi
Tesi di laurea magistrale
Autore
TIRABASSO, DAVIDE
URN
etd-12292023-153451
Titolo
Development of algorithms for the generation of multi-parametric maps based on electrophysiological signals of patients with Brugada syndrome
Dipartimento
INGEGNERIA DELL'INFORMAZIONE
Corso di studi
INGEGNERIA BIOMEDICA
Relatori
relatore Prof. Vozzi, Giovanni
relatore Prof. Positano, Vincenzo
relatore Dott.ssa Hartwig, Valentina
Parole chiave
  • area calculation
  • Brugada syndrome
  • CARTO3
  • multi-parametric maps
  • OpenEP
  • pre/post Ajmaline Test analysis
  • ROI selection
Data inizio appello
14/02/2024
Consultabilità
Non consultabile
Data di rilascio
14/02/2027
Riassunto
Brugada syndrome (BrS) is a rare hereditary heart disease responsible for malignant arrhythmias and sudden cardiac death. The generality of the symptoms shown by the disease are therefore an obstacle in the assessment of the real risk status of the patient, and therefore could lead to a wrong therapeutic choice. For patients with ventricular events at high risk, it is preferred the implantation of an ICD (Implantable Cardioverter-Defibrillator), or alternatively drug therapy and epicardial ablation. The research has been undertaken to provide physicians with suitable tools to conduct a more complete electrophysiological study of the right ventricle, which is essential to deciding the therapeutic path suitable for the patient. In this work we extend the functionalities of the OpenEP (Open Electrophysiology Interface for Research) platform in order to process endocardial electrophysiological signals acquired with the CARTO3 system from BrS patients at Fondazione Toscana Gabriele Monasterio in Pisa, calculating the parameters of interest and creating the respective multi-parametric maps of the right ventricle (RV). In addition, we develop algorithms able to process these parametric maps according to the user requirements defined with the clinical part, providing them with valid support (for example calculating the area where a parameter verifies a condition, or being able to select a ROI on the RV surface). In this way a deep analysis of the electrophysiological condition of the right ventricle is guaranteed, which can be useful both for research purposes and to set a suitable patient-specific therapeutic path.
File