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Aurore Lyon

BHF CRE Graduate Student

Project Title: Computational detection of electrophysiological abnormalities in hypertrophic cardiomyopathy

Supervisors: Prof. Blanca Rodriguez, Dr. Ana Minchole

Biography

I am interested in using computational methods such as modelling, clustering, machine learning and simulations to analyse and interpret cardiovascular data for risk stratification, individual patient management and drug discovery. These methods can help dissect pehnotypic heterogeneity and understand the influence of various cardiac mechanisms in arrhythmogenesis and drug cardiotoxicity.

My DPhil project focused on the development of computational methods applied to electrocardiogram (ECG) data to identify different phenotypes in hypertrophic cardiomyopathy, with differences in arrhythmic risk. Cardiac structural and electrophysiological mechanisms behind these ECG abnormalities were then investigated using computer simulations.

Undergraduate degree: Engineering, at the University Telecom ParisTech

Graduate studentship dates: October 2014- September 2017

 

Publications

A. Lyon, A. Minchole, P. Laguna, J P Martinez, B. Rodriguez. Computational techniques for ECG analysis in light of their contribution to medical advances. J. R. Soc. Interface. 2018 15 20170821; DOI: 10.1098/rsif.2017.0821.

A. Lyon, A. Minchole, E. Passini, B. Rodriguez. Investigation of the Presence and Mechanisms of Action Potential Alternans in Hypertrophic Cardiomyopathy. Computing in Cardiology. 2017.

A. Lyon, R. Ariga, A. Minchole, P. Laguna, N. de Freitas, S. Neubauer, H. Watkins, B. Rodriguez. Risk stratification in hypertrophic cardiomyopathy based on QRS and T wave morphological biomarkers identifies three phenotypic subgroups. European Heart Journal (2016), 37 (Abstract Supplement), 412-413. 2016

A. Lyon, A. Minchole, R. Ariga, P. Laguna, N. de Freitas, S. Neubauer, H. Watkins, B. Rodriguez. Extraction of morphological QRS-based biomarkers in hypertrophic cardiomyopathy for risk stratification using L1 regularized logistic regression. Computing in Cardiology. DOI: 10.1109/CIC.2015.7408573. 2015