12 research outputs found
Application of artificial intelligence techniques for automated detection of myocardial infarction: A review
Myocardial infarction (MI) results in heart muscle injury due to receiving
insufficient blood flow. MI is the most common cause of mortality in
middle-aged and elderly individuals around the world. To diagnose MI,
clinicians need to interpret electrocardiography (ECG) signals, which requires
expertise and is subject to observer bias. Artificial intelligence-based
methods can be utilized to screen for or diagnose MI automatically using ECG
signals. In this work, we conducted a comprehensive assessment of artificial
intelligence-based approaches for MI detection based on ECG as well as other
biophysical signals, including machine learning (ML) and deep learning (DL)
models. The performance of traditional ML methods relies on handcrafted
features and manual selection of ECG signals, whereas DL models can automate
these tasks. The review observed that deep convolutional neural networks
(DCNNs) yielded excellent classification performance for MI diagnosis, which
explains why they have become prevalent in recent years. To our knowledge, this
is the first comprehensive survey of artificial intelligence techniques
employed for MI diagnosis using ECG and other biophysical signals.Comment: 16 pages, 8 figure
Artificial Intelligence-Enabled ECG Algorithm Based on Improved Residual Network for Wearable ECG
Heart disease is the leading cause of death for men and women globally. The residual network (ResNet) evolution of electrocardiogram (ECG) technology has contributed to our understanding of cardiac physiology. We propose an artificial intelligence-enabled ECG algorithm based on an improved ResNet for a wearable ECG. The system hardware consists of a wearable ECG with conductive fabric electrodes, a wireless ECG acquisition module, a mobile terminal App, and a cloud diagnostic platform. The algorithm adopted in this study is based on an improved ResNet for the rapid classification of different types of arrhythmia. First, we visualize ECG data and convert one-dimensional ECG signals into two-dimensional images using Gramian angular fields. Then, we improve the ResNet-50 network model, add multistage shortcut branches to the network, and optimize the residual block. The ReLu activation function is replaced by a scaled exponential linear units (SELUs) activation function to improve the expression ability of the model. Finally, the images are input into the improved ResNet network for classification. The average recognition rate of this classification algorithm against seven types of arrhythmia signals (atrial fibrillation, atrial premature beat, ventricular premature beat, normal beat, ventricular tachycardia, atrial tachycardia, and sinus bradycardia) is 98.3%
Analysis of Ventricular Depolarisation and Repolarisation Using Registration and Machine Learning
Our understanding of cardiac diseases has greatly advanced since the advent of electrocardiography (ECG). With the increasing influx of available data in recent times, significant research efforts have been put forth to automate the study and detection of cardiac conditions. Naturally, the focus has progressed toward studying dynamic changes in ventricular depolarisation and repolarisation across serial recordings - as complex beat-to-beat changes in morphology manifest over time. Manual extraction of diagnostic and prognostic markers is a laborious task. Hence, automated and accurate methods are required to extract markers for the study of ventricular lability and detection of common diseases, such as myocardial ischemia and myocardial infarction. The aim of this thesis is to improve automated marker extraction and detection of diseases for the study of ventricular depolarisation and repolarisation lability in ECG. As such, two novel template adaptation methods capable of capturing complex beat-to-beat morphological changes are proposed for three-dimensional and two-dimensional data, respectively. The proposed three-dimensional template adaptation method provides an inhomogeneous method for transforming template vectorcardiogram (VCG) by exploiting registrationinspired parametrisation and an efficient kernel ridge regression formulation. Analysis across simulated data and clinical myocardial infarction data demonstrates state-of-the-art results. The two-dimensional template adaptation method draws from traditional registrationbased techniques and treats the ECG as a two-dimensional point set problem. Validation against previously employed simulated data and a gold-standard annotated clinical database demonstrate the highest level of performance. Subsequently, frameworks employing the proposed template adaptation techniques are developed for the automated detection of ischemic beats and myocardial infarction. Furthermore, a small study analysing ventricular repolarisation variability (VRV) in non-ischemic cardiomyopathy (CM) is considered, utilising markers of cardiac lability proposed in the development of the three-dimensional template adaptation system. In summary, this thesis highlights the necessity for custom template adaptation methods for the accurate measurement of beat-to-beat variability in cardiac data. Two novel stateof- the-art methods are proposed and extended to study myocardial ischemia, myocardial infarction and non-ischemic CM.Thesis (Ph.D.) -- University of Adelaide, School of Electrical and Electronic Engineering, 202
Automatic Spatiotemporal Analysis of Cardiac Image Series
RĂSUMĂ
Ă ce jour, les maladies cardiovasculaires demeurent au premier rang des principales causes de
dĂ©cĂšs en AmĂ©rique du Nord. Chez lâadulte et au sein de populations de plus en plus jeunes,
la soi-disant Ă©pidĂ©mie dâobĂ©sitĂ© entraĂźnĂ©e par certaines habitudes de vie tels que la mauvaise
alimentation, le manque dâexercice et le tabagisme est lourde de consĂ©quences pour les personnes
affectées, mais aussi sur le systÚme de santé. La principale cause de morbidité et de
mortalitĂ© chez ces patients est lâathĂ©rosclĂ©rose, une accumulation de plaque Ă lâintĂ©rieur des
vaisseaux sanguins à hautes pressions telles que les artÚres coronaires. Les lésions athérosclérotiques
peuvent entraĂźner lâischĂ©mie en bloquant la circulation sanguine et/ou en provoquant
une thrombose. Cela mĂšne souvent Ă de graves consĂ©quences telles quâun infarctus. Outre les
problÚmes liés à la sténose, les parois artérielles des régions criblées de plaque augmentent la
rigidité des parois vasculaires, ce qui peut aggraver la condition du patient. Dans la population
pédiatrique, la pathologie cardiovasculaire acquise la plus fréquente est la maladie de
Kawasaki. Il sâagit dâune vasculite aigĂŒe pouvant affecter lâintĂ©gritĂ© structurale des parois des
artĂšres coronaires et mener Ă la formation dâanĂ©vrismes. Dans certains cas, ceux-ci entravent
lâhĂ©modynamie artĂ©rielle en engendrant une perfusion myocardique insuffisante et en activant
la formation de thromboses.
Le diagnostic de ces deux maladies coronariennes sont traditionnellement effectuĂ©s Ă lâaide
dâangiographies par fluoroscopie. Pendant ces examens paracliniques, plusieurs centaines de
projections radiographiques sont acquises en sĂ©ries suite Ă lâinfusion artĂ©rielle dâun agent de
contraste. Ces images révÚlent la lumiÚre des vaisseaux sanguins et la présence de lésions
potentiellement pathologiques, sâil y a lieu. Parce que les sĂ©ries acquises contiennent de lâinformation
trĂšs dynamique en termes de mouvement du patient volontaire et involontaire (ex.
battements cardiaques, respiration et dĂ©placement dâorganes), le clinicien base gĂ©nĂ©ralement
son interprĂ©tation sur une seule image angiographique oĂč des mesures gĂ©omĂ©triques sont effectuĂ©es
manuellement ou semi-automatiquement par un technicien en radiologie. Bien que
lâangiographie par fluoroscopie soit frĂ©quemment utilisĂ© partout dans le monde et souvent
considĂ©rĂ© comme lâoutil de diagnostic âgold-standardâ pour de nombreuses maladies vasculaires,
la nature bidimensionnelle de cette modalitĂ© dâimagerie est malheureusement trĂšs
limitante en termes de spécification géométrique des différentes régions pathologiques. En effet,
la structure tridimensionnelle des stĂ©noses et des anĂ©vrismes ne peut pas ĂȘtre pleinement
appréciée en 2D car les caractéristiques observées varient selon la configuration angulaire de
lâimageur. De plus, la prĂ©sence de lĂ©sions affectant les artĂšres coronaires peut ne pas reflĂ©ter
la véritable santé du myocarde, car des mécanismes compensatoires naturels (ex. vaisseaux----------ABSTRACT
Cardiovascular disease continues to be the leading cause of death in North America. In adult
and, alarmingly, ever younger populations, the so-called obesity epidemic largely driven by
lifestyle factors that include poor diet, lack of exercise and smoking, incurs enormous stresses
on the healthcare system. The primary cause of serious morbidity and mortality for these
patients is atherosclerosis, the build up of plaque inside high pressure vessels like the coronary
arteries. These lesions can lead to ischemic disease and may progress to precarious blood
flow blockage or thrombosis, often with infarction or other severe consequences. Besides
the stenosis-related outcomes, the arterial walls of plaque-ridden regions manifest increased
stiffness, which may exacerbate negative patient prognosis. In pediatric populations, the
most prevalent acquired cardiovascular pathology is Kawasaki disease. This acute vasculitis
may affect the structural integrity of coronary artery walls and progress to aneurysmal lesions.
These can hinder the blood flowâs hemodynamics, leading to inadequate downstream
perfusion, and may activate thrombus formation which may lead to precarious prognosis.
Diagnosing these two prominent coronary artery diseases is traditionally performed using
fluoroscopic angiography. Several hundred serial x-ray projections are acquired during selective
arterial infusion of a radiodense contrast agent, which reveals the vesselsâ luminal
area and possible pathological lesions. The acquired series contain highly dynamic information
on voluntary and involuntary patient movement: respiration, organ displacement and
heartbeat, for example. Current clinical analysis is largely limited to a single angiographic
image where geometrical measures will be performed manually or semi-automatically by a
radiological technician. Although widely used around the world and generally considered
the gold-standard diagnosis tool for many vascular diseases, the two-dimensional nature of
this imaging modality is limiting in terms of specifying the geometry of various pathological
regions. Indeed, the 3D structures of stenotic or aneurysmal lesions may not be fully appreciated
in 2D because their observable features are dependent on the angular configuration of
the imaging gantry. Furthermore, the presence of lesions in the coronary arteries may not
reflect the true health of the myocardium, as natural compensatory mechanisms may obviate
the need for further intervention. In light of this, cardiac magnetic resonance perfusion
imaging is increasingly gaining attention and clinical implementation, as it offers a direct
assessment of myocardial tissue viability following infarction or suspected coronary artery
disease. This type of modality is plagued, however, by motion similar to that present in fluoroscopic
imaging. This issue predisposes clinicians to laborious manual intervention in order
to align anatomical structures in sequential perfusion frames, thus hindering automation o
Automatic Spatiotemporal Analysis of Cardiac Image Series
RĂSUMĂ
Ă ce jour, les maladies cardiovasculaires demeurent au premier rang des principales causes de
dĂ©cĂšs en AmĂ©rique du Nord. Chez lâadulte et au sein de populations de plus en plus jeunes,
la soi-disant Ă©pidĂ©mie dâobĂ©sitĂ© entraĂźnĂ©e par certaines habitudes de vie tels que la mauvaise
alimentation, le manque dâexercice et le tabagisme est lourde de consĂ©quences pour les personnes
affectées, mais aussi sur le systÚme de santé. La principale cause de morbidité et de
mortalitĂ© chez ces patients est lâathĂ©rosclĂ©rose, une accumulation de plaque Ă lâintĂ©rieur des
vaisseaux sanguins à hautes pressions telles que les artÚres coronaires. Les lésions athérosclérotiques
peuvent entraĂźner lâischĂ©mie en bloquant la circulation sanguine et/ou en provoquant
une thrombose. Cela mĂšne souvent Ă de graves consĂ©quences telles quâun infarctus. Outre les
problÚmes liés à la sténose, les parois artérielles des régions criblées de plaque augmentent la
rigidité des parois vasculaires, ce qui peut aggraver la condition du patient. Dans la population
pédiatrique, la pathologie cardiovasculaire acquise la plus fréquente est la maladie de
Kawasaki. Il sâagit dâune vasculite aigĂŒe pouvant affecter lâintĂ©gritĂ© structurale des parois des
artĂšres coronaires et mener Ă la formation dâanĂ©vrismes. Dans certains cas, ceux-ci entravent
lâhĂ©modynamie artĂ©rielle en engendrant une perfusion myocardique insuffisante et en activant
la formation de thromboses.
Le diagnostic de ces deux maladies coronariennes sont traditionnellement effectuĂ©s Ă lâaide
dâangiographies par fluoroscopie. Pendant ces examens paracliniques, plusieurs centaines de
projections radiographiques sont acquises en sĂ©ries suite Ă lâinfusion artĂ©rielle dâun agent de
contraste. Ces images révÚlent la lumiÚre des vaisseaux sanguins et la présence de lésions
potentiellement pathologiques, sâil y a lieu. Parce que les sĂ©ries acquises contiennent de lâinformation
trĂšs dynamique en termes de mouvement du patient volontaire et involontaire (ex.
battements cardiaques, respiration et dĂ©placement dâorganes), le clinicien base gĂ©nĂ©ralement
son interprĂ©tation sur une seule image angiographique oĂč des mesures gĂ©omĂ©triques sont effectuĂ©es
manuellement ou semi-automatiquement par un technicien en radiologie. Bien que
lâangiographie par fluoroscopie soit frĂ©quemment utilisĂ© partout dans le monde et souvent
considĂ©rĂ© comme lâoutil de diagnostic âgold-standardâ pour de nombreuses maladies vasculaires,
la nature bidimensionnelle de cette modalitĂ© dâimagerie est malheureusement trĂšs
limitante en termes de spécification géométrique des différentes régions pathologiques. En effet,
la structure tridimensionnelle des stĂ©noses et des anĂ©vrismes ne peut pas ĂȘtre pleinement
appréciée en 2D car les caractéristiques observées varient selon la configuration angulaire de
lâimageur. De plus, la prĂ©sence de lĂ©sions affectant les artĂšres coronaires peut ne pas reflĂ©ter
la véritable santé du myocarde, car des mécanismes compensatoires naturels (ex. vaisseaux----------ABSTRACT
Cardiovascular disease continues to be the leading cause of death in North America. In adult
and, alarmingly, ever younger populations, the so-called obesity epidemic largely driven by
lifestyle factors that include poor diet, lack of exercise and smoking, incurs enormous stresses
on the healthcare system. The primary cause of serious morbidity and mortality for these
patients is atherosclerosis, the build up of plaque inside high pressure vessels like the coronary
arteries. These lesions can lead to ischemic disease and may progress to precarious blood
flow blockage or thrombosis, often with infarction or other severe consequences. Besides
the stenosis-related outcomes, the arterial walls of plaque-ridden regions manifest increased
stiffness, which may exacerbate negative patient prognosis. In pediatric populations, the
most prevalent acquired cardiovascular pathology is Kawasaki disease. This acute vasculitis
may affect the structural integrity of coronary artery walls and progress to aneurysmal lesions.
These can hinder the blood flowâs hemodynamics, leading to inadequate downstream
perfusion, and may activate thrombus formation which may lead to precarious prognosis.
Diagnosing these two prominent coronary artery diseases is traditionally performed using
fluoroscopic angiography. Several hundred serial x-ray projections are acquired during selective
arterial infusion of a radiodense contrast agent, which reveals the vesselsâ luminal
area and possible pathological lesions. The acquired series contain highly dynamic information
on voluntary and involuntary patient movement: respiration, organ displacement and
heartbeat, for example. Current clinical analysis is largely limited to a single angiographic
image where geometrical measures will be performed manually or semi-automatically by a
radiological technician. Although widely used around the world and generally considered
the gold-standard diagnosis tool for many vascular diseases, the two-dimensional nature of
this imaging modality is limiting in terms of specifying the geometry of various pathological
regions. Indeed, the 3D structures of stenotic or aneurysmal lesions may not be fully appreciated
in 2D because their observable features are dependent on the angular configuration of
the imaging gantry. Furthermore, the presence of lesions in the coronary arteries may not
reflect the true health of the myocardium, as natural compensatory mechanisms may obviate
the need for further intervention. In light of this, cardiac magnetic resonance perfusion
imaging is increasingly gaining attention and clinical implementation, as it offers a direct
assessment of myocardial tissue viability following infarction or suspected coronary artery
disease. This type of modality is plagued, however, by motion similar to that present in fluoroscopic
imaging. This issue predisposes clinicians to laborious manual intervention in order
to align anatomical structures in sequential perfusion frames, thus hindering automation o
Deep learning for time series classification
Time series analysis is a field of data science which is interested in
analyzing sequences of numerical values ordered in time. Time series are
particularly interesting because they allow us to visualize and understand the
evolution of a process over time. Their analysis can reveal trends,
relationships and similarities across the data. There exists numerous fields
containing data in the form of time series: health care (electrocardiogram,
blood sugar, etc.), activity recognition, remote sensing, finance (stock market
price), industry (sensors), etc. Time series classification consists of
constructing algorithms dedicated to automatically label time series data. The
sequential aspect of time series data requires the development of algorithms
that are able to harness this temporal property, thus making the existing
off-the-shelf machine learning models for traditional tabular data suboptimal
for solving the underlying task. In this context, deep learning has emerged in
recent years as one of the most effective methods for tackling the supervised
classification task, particularly in the field of computer vision. The main
objective of this thesis was to study and develop deep neural networks
specifically constructed for the classification of time series data. We thus
carried out the first large scale experimental study allowing us to compare the
existing deep methods and to position them compared other non-deep learning
based state-of-the-art methods. Subsequently, we made numerous contributions in
this area, notably in the context of transfer learning, data augmentation,
ensembling and adversarial attacks. Finally, we have also proposed a novel
architecture, based on the famous Inception network (Google), which ranks among
the most efficient to date.Comment: PhD thesi
Seamless Multimodal Biometrics for Continuous Personalised Wellbeing Monitoring
Artificially intelligent perception is increasingly present in the lives of
every one of us. Vehicles are no exception, (...) In the near future, pattern
recognition will have an even stronger role in vehicles, as self-driving cars
will require automated ways to understand what is happening around (and within)
them and act accordingly. (...) This doctoral work focused on advancing
in-vehicle sensing through the research of novel computer vision and pattern
recognition methodologies for both biometrics and wellbeing monitoring. The
main focus has been on electrocardiogram (ECG) biometrics, a trait well-known
for its potential for seamless driver monitoring. Major efforts were devoted to
achieving improved performance in identification and identity verification in
off-the-person scenarios, well-known for increased noise and variability. Here,
end-to-end deep learning ECG biometric solutions were proposed and important
topics were addressed such as cross-database and long-term performance,
waveform relevance through explainability, and interlead conversion. Face
biometrics, a natural complement to the ECG in seamless unconstrained
scenarios, was also studied in this work. The open challenges of masked face
recognition and interpretability in biometrics were tackled in an effort to
evolve towards algorithms that are more transparent, trustworthy, and robust to
significant occlusions. Within the topic of wellbeing monitoring, improved
solutions to multimodal emotion recognition in groups of people and
activity/violence recognition in in-vehicle scenarios were proposed. At last,
we also proposed a novel way to learn template security within end-to-end
models, dismissing additional separate encryption processes, and a
self-supervised learning approach tailored to sequential data, in order to
ensure data security and optimal performance. (...)Comment: Doctoral thesis presented and approved on the 21st of December 2022
to the University of Port