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An ECG and echocardiography-based characterization of hypertrophic cardiomyopathy using machine learning

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dc.contributor.author Leal Bajo, Carla
dc.date.accessioned 2023-09-22T17:31:31Z
dc.date.available 2023-09-22T17:31:31Z
dc.date.issued 2023-09-22
dc.identifier.uri http://hdl.handle.net/10230/57948
dc.description Tutors: Dr. Bart Bijnens, MSc. Pablo-Miki Martí Castellote. Treball de fi de grau en Biomèdica
dc.description.abstract Hypertrophic cardiomyopathy (HCM) is a condition characterized by an abnormal thickening of the myocardium resulting in impaired blood pumping efficiency of the heart. HCM exhibits clinical variability, with diverse symptoms and genetic backgrounds, resulting in pathophysiological complexity not fully captured by current clinical guidelines. Machine learning (ML) techniques have demonstrated utility in understanding and classifying heart-related disorders, such as heart failure, as well as identifying distinct phenogroups within various conditions. This project aims to apply unsupervised Multiple Kernel Learning (MKL) to characterize HCM patients based on electrocardiogram (ECG) and echocardiography measurements, contributing to a deeper understanding of HCM’s underlying pathophysiological mechanisms and the identification of phenogroups. More precisely, the project analyzes a single-center cohort comprising phenotypeand genotype-positive (G+) HCM patients (n = 73) and their phenotype-negative relatives (n =32, 50% G+). All patients underwent digital 12-lead ECG, echocardiography, and a magnetic resonance (CMR) studies. This analysis is based on the reduction of data dimensionality using unsupervised MKL to combine the information from the input data to generate a space of reduced dimensions. Five different input data combinations are utilized: (1) using only echocardiography measurements, (2) using only ECG complete cycles, (3) using only ECG segmented waves, (4) using echocardiography measurements and ECG complete cycles and (5) using echocardiography measurements and ECG segmented cycles. Regression to the input descriptors as a function of the newly computed dimensions enables interpretation of how variance is represented in the new space. Subsequently, clustering is performed in the resulting space, yielding four homogeneous patient groups that help address heterogeneity within the overall population
dc.format.mimetype application/pdf
dc.language eng
dc.language.iso eng
dc.rights Llicència CC Reconeixement-NoComercial-SenseObraDerivada 4.0 Internacional (CC BY-NC-ND 4.0)
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/deed.ca
dc.title An ECG and echocardiography-based characterization of hypertrophic cardiomyopathy using machine learning
dc.type info:eu-repo/semantics/bachelorThesis
dc.subject.keyword Hypertrophic cardiomyopathy
dc.subject.keyword Echocardiography
dc.subject.keyword Regional longitudinal strains
dc.subject.keyword ECG
dc.subject.keyword Machine learning
dc.subject.keyword Unsupervised multiple Kernel learning
dc.subject.keyword Dimensionality reduction
dc.subject.keyword Kernel regression
dc.subject.keyword Clustering
dc.subject.keyword Phenogroups
dc.rights.accessRights info:eu-repo/semantics/openAccess


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