computational detection of electrophysiological abnormalities in hypertrophic cardiomyopathy for ...

218 Pages·2017·8.76 MB·English

by  Aurore Lyon

C D
OMPUTATIONAL ETECTION OF
E A
LECTROPHYSIOLOGICAL BNORMALITIES
H C
IN YPERTROPHIC ARDIOMYOPATHY FOR
R S
ISK TRATIFICATION

Aurore Lyon
St Hugh’s College

Computational Cardiovascular Science Group
Department of Computer Science

University of Oxford
Michaelmas 2017

This thesis is submitted to the Department of Computer Science, University of Oxford,
for the degree of Doctor of Philosophy. This thesis is entirely my own work, and,
except where otherwise indicated, describes my own research

***
To my parents
Avec une pensée pour Claude et Philippe
***

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Aurore Lyon Doctor of Philosophy
St Hugh’s College Michaelmas Term 2017

COMPUTATIONAL DETECTION OF ELECTROPHYSIOLOGICAL
ABNORMALITIES IN HYPERTROPHIC CARDIOMYOPATHY FOR
RISK STRATIFICATION

ABSTRACT
Hypertrophic cardiomyopathy (HCM) is a common cardiac genetic disease and a major
cause of sudden cardiac death (SCD) in young adults. While most patients remain
asymptomatic, others suffer from SCD triggered by ventricular arrhythmias. An accurate
detection of these high-risk patients in order to provide them with appropriate treatment is
therefore essential, and it remains a challenge as current electrocardiogram (ECG)
biomarkers are not specific. In this thesis, we develop computational methods for the
analysis and interpretation of electrophysiological clinical data in order to investigate the
diversity of HCM phenotypes with the ultimate aim to improve risk stratification in HCM.
First, by combining computational clustering and mathematical modelling, we identify four
distinct ECG phenotypes exhibiting differences in hypertrophy distribution and risk of
arrhythmia. The group with primary repolarization abnormalities and coexistence of apical
and septal hypertrophy shows a higher HCM Risk-SCD score compared to other groups.
Second, we explore the influence of structural and electrophysiological mechanisms on the
ECG to explain the four HCM phenotypes identified, by using a whole-body personalized
3D computer simulation framework. We show that apico-basal repolarization
heterogeneities explain the T wave inversions in the high risk group, and that an abnormal
Purkinje system may explain the QRS abnormalities in another group. Finally, we further
investigate the presence of repolarization biomarkers in HCM, specifically action potential
alternans, and show that HCM cardiomyocytes do not overall exhibit more action potential
alternans compared to controls, which may suggest other whole-organ mechanisms
responsible for T wave alternans. Overall, this thesis contributes towards a better
understanding of HCM phenotypic heterogeneity and the improvement of individual
patient management.
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Publications
The publications resulting from the work described in this thesis are listed below.
 A. Lyon, A. Mincholé, J P. Martínez, P. Laguna and 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, R. Ariga, A. Mincholé, M. Mahmod, E. Ormondroyd, P. Laguna, N. de
Freitas, S. Neubauer, H. Watkins and B. Rodriguez. Distinct ECG phenotypes
identified in hypertrophic cardiomyopathy using machine learning associate with
arrhythmic risk markers. Front Physiol. 2018; 9:213. DOI:
10.3389/fphys.2018.00213
 A. Lyon, A. Mincholé. E. Passini and B. Rodriguez. Investigation of the Presence
and Mechanisms of Action Potential Alternans in Hypertrophic Cardiomyopathy.
Computing in Cardiology. 2017.
 A. Lyon et al. Investigation of the effect of structural abnormalities on the ECG in
hypertrophic cardiomyopathy patients using MRI-based computer models of the
human heart and torso. Submitted to Europace.
 A. Lyon, R. Ariga, A. Mincholé, 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, 37 (Abstract Supplement), 412-413. 2016
 A. Ghetti, V. Grau, S. Harmer, I. Kopljar, P. Lambiase, H. Rong Lu, A. Lyon, A.
Minchole, A. Muszkiewicz, J. Oster, M. Paci, E. Passini, S. Severi, P. Taggart, A.
Tinker, J.-P. Valentin, A. Varro, M. Wallman, X. Zhou, Human-based approaches
to pharmacology and cardiology: an interdisciplinary and intersectorial workshop.
Europace 18(9). 2016.
 A. Lyon, A. Mincholé, 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
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Conference presentations
The work described in this thesis was presented at the following conferences:
 CMR2018, Barcelona, February 2018 (invited talk)
 Computing in Cardiology, Rennes, September 2017 (talk)
 BHF Centre for Research Excellence Symposium – Oxford, September 2017
(invited talk)
 European Medical and Biological Engineering Conference (EMBEC),
Tampere, June 2017 (poster)
 BHF Centre for Research Excellence Symposium – Oxford, September 2016
(poster)
 European Society of Cardiology (ESC) Congress, Rome, August 2016 (talk)
 Young Investigator Award Competition at Computing in Cardiology, Nice,
2015 (talk)
 BHF Centre for Research Excellence Symposium – Oxford, September 2014
(poster)
Prizes
The work described in this thesis was awarded the following prizes:
 Best talk, Oxford and Cambridge Women in Computer Science Conference,
Oxford, March 2017
 Winner of the Young Investigator Award, Computing in Cardiology, Nice,
September 2015
 Honorary mention for Best Poster, BHF Centre for Research Excellence
Symposium, Oxford, September 2014

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ACKNOWLEDGEMENTS

First of all, I would like to thank my supervisors, Professor Blanca Rodríguez and Dr Ana
Mincholé, for all their support over the last three years. Ana, thank you for being available,
supportive, reassuring and optimistic every single day. Most of this work would never have
been done without your help! Blanca, you have been an incredible model to look up to and
I have learnt so much by your side. Thank you for supporting me through failure and
doubt, for reminding me of the big picture when I needed it, and for teaching me life
lessons I will not forget!
The work presented here would not exist without the help and support of many
collaborators. Special thanks to Dr Rina Ariga, for providing us with the rich dataset used
in this work, and for all her hard work and patience. I would also like to thank Professor
Hugh Watkins for his implication in this research, Dr Ernesto Zacur, for all the work and
guidance on the imaging work, Professor Nando de Freitas, for his fruitful ideas, and
Professor Pablo Laguna. Pablo, muchas gracias por todas las discusiones tan productivas
que hemos tenido, tus geniales ideas y sobre todo por tu apoyo y generosidad. Thank you
to the Department of Computer Science, for a fantastic work environment, and the British
Heart Foundation for funding my DPhil.
A big thank you to all my friends who made these three years amazing: Laure, for being
such a good friend, and the best food and sport mate ever, Héctor, for all your
inappropriate jokes and your continuous patience with me, Cristian, for your never-ending
Italian energy, Anna, my DPhil mum, Nicolas, my best tennis partner, and everyone in the
CCS group for making everyday life in the office so nice. Thanks to my friends back in
France: Emilie, Adrien, Tatiana, Nils, for staying in touch despite my rare trips back to
Paris. Thank you to St Hugh’s College and its MCR for making me feel part of the family,
and to all the sporting teams I was lucky to join, especially OULTC, and OULGC.
Separately, thank you to Kayla Friedman and Malcolm Morgan of the Centre for
Sustainable Development, University of Cambridge, for the Microsoft Word thesis
template used to produce this document.
Finally, I would like to thank all my family, especially my mum, dad and sister for
supporting me every step of the way!
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CONTENTS

PUBLICATIONS .............................................................................................................. IV
CONFERENCE PRESENTATIONS ....................................................................................... V
PRIZES ........................................................................................................................... V
1 INTRODUCTION ....................................................................................................... 1
1.1 INTRODUCTION ........................................................................................................ 1
1.2 THESIS AIMS ............................................................................................................. 4
1.3 THESIS OUTLINE ....................................................................................................... 5
2 CARDIAC ELECTROPHYSIOLOGY AND HYPERTROPHIC
CARDIOMYOPATHY .................................................................................................. 8
2.1 INTRODUCTION ........................................................................................................ 8
2.2 ELECTROPHYSIOLOGY OF THE HEALTHY HEART ....................................................... 9
2.2.1 Cardiac anatomy and function ............................................................................. 9
2.2.2 Cardiac electrical propagation: from single cell to whole organ ...................... 11
2.2.3 Electrocardiogram recording ............................................................................. 15
2.3 HYPERTROPHIC CARDIOMYOPATHY (HCM) ........................................................... 19
2.3.1 HCM structural and electrophysiological abnormalities ................................... 20
2.3.2 Risk stratification in HCM .................................................................................. 23
2.3.3 ECG abnormalities and standard biomarkers in HCM ...................................... 25
2.3.4 Imaging techniques for the identification of HCM structural abnormalities ..... 26
3 COMPUTATIONAL METHODS FOR ELECTROCARDIOGRAM ANALYSIS
AND INTERPRETATION .......................................................................................... 28
3.1 INTRODUCTION ...................................................................................................... 28
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3.2 SIGNAL PROCESSING TECHNIQUES FOR ECG SIGNALS ............................................ 29
3.2.1 Noise removal and filtering of Holter recordings ............................................... 29
3.2.2 Delineation of ECG waveforms .......................................................................... 31
3.3 MACHINE LEARNING TECHNIQUES FOR CLASSIFICATION OF ECG SIGNALS ............ 38
3.3.1 Objectives of state of the art methods and available databases ......................... 38
3.3.2 Feature extraction and dimensionality reduction ............................................... 40
3.3.3 Main machine learning techniques for ECG classification ................................ 43
3.3.4 Discussion ........................................................................................................... 54
3.4 PERSONALIZED WHOLE-BODY COMPUTER SIMULATIONS ........................................ 56
3.4.1 Modelling of cardiac anatomy ............................................................................ 57
3.4.2 Modelling of the cardiac electrical propagation ................................................ 59
3.4.3 Modelling of cardiac heterogeneities ................................................................. 62
4 EXTRACTION OF ECG PHENOTYPES IN HCM BASED ON QRS
MORPHOLOGICAL BIOMARKERS ...................................................................... 64
4.1 INTRODUCTION ....................................................................................................... 64
4.2 MOTIVATION .......................................................................................................... 65
4.3 METHODS ............................................................................................................... 66
4.3.1 Clinical database ................................................................................................ 66
4.3.2 ECG signal processing ....................................................................................... 68
4.3.3 Extraction of standard and Hermite based biomarkers ...................................... 71
4.3.4 Binary classification between HCM and controls .............................................. 74
4.3.5 Dimensionality reduction and clustering ............................................................ 75
4.3.6 Statistical analysis .............................................................................................. 77
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