Analysis of Electrocardiogram signals : from modelling to classification through signal processing
Year: 2022
Type: dissertation
Abstract: The focus of my research is the use of modeling and signal-processing methods to extract novel clinical parameters from large databases of physiological signals, and the use of machine-learning techniques to provide predictive actionable information to clinicians. I have been applying these techniques to electrocardiographic (ECG) (and other physiological) data for more than 10 years. My research project consists in developing novel machine-learning techniques combined with and/or inspired by modelling in order to create interpretable automatic decisionmaking systems in healthcare, with a particular focus to cardiovascular health data. Clinical assessment of the cardiovascular heath requires the acquisition of multimodal data, two of the most important modalities being Magnetic Resonance Imaging (MRI) and ECG. I therefore aim at developing tools for the joint analysis of electrophysiological and imaging data. These techniques and representations of data will offer solutions for concrete clinical problems such as better risk stratification for patients, whether for Atrial Fibrillation catheter ablation outcome prediction, and the risk of presenting Ventricular Tachycardia.
Source: HAL
Author: Oster, Julien
Language: English
Topic: ECG Monitoring and Analysis
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W4392491374