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

Leal Bajo, Carla

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

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Leal Bajo, C. An ECG and echocardiography-based characterization of hypertrophic cardiomyopathy using machine learning. 2023. handle: http://hdl.handle.net/10230/57948

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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