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Identifier 000461901
Title Applications of machine learning and computational methods in the prediction of cardiovascular remodeling
Alternative Title Εφαρμογές μηχανικής μάθησης και υπολογιστικών μεθόδων στην πρόβλεψη της καρδιοαγγειακής αναδιαμόρφωσης
Author Αγγελάκη, Ελένη Ε.
Thesis advisor Τσιρώνης, Γεώργιος
Reviewer Μαρκέτου, Μαρία
Ζέζας, Ανδρέας
Κοχιαδάκης, Γεώργιος
Κομίνης, Ιωάννης
Μακρής, Κωνσταντίνος
Λαγαρής, Ισαάκ
Abstract Machine learning (ML) is a growing field poised to change the way we practice cardiovascular medicine, providing new tools for interpreting data and making decisions. Cardiac digital images or biosignals defy traditional statistical methods and require the deployment of ML. In this work we show that ML classifiers trained using anthropometric features and ECG-derived features, can: (a) detect abnormal left ventricular geometry, even before the onset of left ventricular hypertrophy; (b) detect hypertension using 12-lead-ECG-derived features; and (c) detect hypertension in populations without cardiovascular disease from single-lead-ECGs. The latter classifiers can be useful in raising awareness in people with undiagnosed hypertension. We then present a computational method to simulate the dynamics of action potential propagation using the three-variable Fenton-Karma model and account for both normal and damaged cells through spatially inhomogeneous voltage diffusion coefficient.
Language English
Subject Artificial intelligence
ECG
Kardiology
Medicine
Ηλεκτροκαρδιογράφημα
Ιατρική
Καρδιολογία
Μηχανική μάθηση
Τεχνητή νοημοσύνη
Issue date 2024-01-15
Collection   School/Department--School of Sciences and Engineering--Department of Physics--Doctoral theses
  Type of Work--Doctoral theses
Permanent Link https://elocus.lib.uoc.gr//dlib/7/f/0/metadata-dlib-1705395431-121837-22315.tkl Bookmark and Share
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