Convex Optimization and Statistical Learning for Non-Invasive Electrical Cardiac Activity Estimation in Atrial Fibrillation
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2021
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Universidad Rey Juan Carlos
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Atrial fibrillation (AF) is the most common type of cardiac arrhythmia in clinical
practice, affecting more than 33 million patients in the world [1]. AF is also a condition
that increases the risk of the patients to suffer embolism, cardiac failure, stroke
and, in the worst of cases, death [2]. Therefore, one of the clinical goals in AF patients
is to restore sinus rhythm. This objective is usually accomplished by ablation of the
cardiac tissue. Main targets of ablation are AF onset locations and drivers responsible
for AF perpetuation [4].
Previous human in vivo research showed different strategies to locate AF drivers
and guide pulmonary veins isolation (PVI). In the case of invasive mapping procedures
[5]–[7], several catheters are introduced inside the atrial chambers to record
from 8 to 128 simultaneous electrograms (EGMs) [5]. Despite the number of intracardiac
signals, the large distance between catheter sensors and the complex atrial
anatomy limits the capability of intracardiac mapping systems to characterize the
global electrical activity in AF [8]. Non-invasive procedures based on ECG imaging
(ECGI) have been also tested to guide PVI [9]–[11].
ECGI has been previously proposed to effectively reconstruct the electrophysiological
activity on the heart surface, solving the spatio-temporal limitations of classical
ECG, by using a non-invasive recording of body surface potentials (BSPs) [15], [16].
ECGI combines both numerical modeling of the bioelectric properties of the thorax
and signal processing. However, ECGI is an ill-posed problem because the propagation
between the epicardium and the torso implies information loss [17], and the
BSPs are also blurred compared to the signals on the heart due to the laws of electromagnetic
field theory. Regularization methods are then needed to obtain reliable
and stable epicardial potential reconstructions [18]–[22]. On the other hand, ECGI
requires an accurate mathematical modeling of both atria and torso, mainly from
computerized tomography (CT) or magnetic resonance (MR) images.
Previous research have shown that including any available a priori information
during electrophysiological studies (EPSs) may improve the estimation of electrical
activity on the heart [18], [29], [30]. Thus, combining non-invasive and invasive
recordings during the ablation procedures may overcome some of the limitations of
each individual technique and lead to more accurate identification of AF drivers.
Moreover, in the last decade, Machine Learning and Deep Learning techniques
have undergone considerable development in bioengineering, and this include novel
research in AF. However, most of Deep Learning-based research which focus on AF
is based on event detection and classification [31]–[33], and they do not include the
estimation of ablation targets. Nonetheless, recent research showed that Machine
Learning and Deep Learning methods can be also used in more complex tasks, like
heart surface potentials estimation from BSPs [34] and rotor identification from 12-
lead ECG [35].
Therefore, the main aim of the present Thesis is to overcome or avoid the limitations
of ECGI in terms of epicardial potential estimation and rotor identification. To
do that, two specific objectives are going to be defined: Inclusion of intracardiac information in ECGI during AF.We are going to assess
new method to incorporate intracardiac information to solve ECGI, that is able
to combine near-field (EGMs) and far-field (BSPs) information to provide a
more complete picture of the electrical activity of the atria during AF. This
objective will be carried out with a Tikhonov-based formulation for inverse
problem that includes EGM-based intracavitary measurements obtained from
the endocardium, simultaneously to BSPs.
Implementation of a non-invasive Deep Learning-based method to estimate AF
driver location. We are going to model the location of AF drivers from BSPs as
a supervised classification problem with Deep Learning techniques, to address
the location of AF drivers from previously-labeled realistic computerized AF
models. The main aim of this point is to predict those regions without using
ECGI, which requires accurate mathematical modeling of torso and atria.
In both cases, we have used realistic tridimensional models for atria and torso,
which simulated different AF propagation patterns. These synthetic cardiac models
included simulated EGMs, from which BSPs were computed using the forward
problem of electrocardiography. To simulate a realistic scenario, gaussian noise was
added to these BSPs.
We first proposed a new method to include intracavitary information from endocardial
EGMs into the ECGI methodology. That is, we have included this information
as an additional constraint on the Tikh regularization method. The proposed model
improved the estimation of epicardial potentials by using first-order Constrained
Tikhonov (Cons-Tikh) method, which was able to reproduce high frequency components
in the solution, avoiding the low-pass filtering behavior of the classical Tikh
approach. However, epicardial potential estimation degraded severely for all the
methods as the complexity of AF increases. Moreover, although this methodology
has two free regularization parameters to be estimated (very computationally expensive),
we have diminished the computational cost by using an iterative regularization
parameter estimation method. Finally, combined BSPs-EGMs measurements could be
easily achieved in a real environment, since several studies simultaneously recorded
BSPs and endocardial EGMs. Therefore, it is possible to integrate both types of signals
in the proposed formulation in a clinical setting.
Then, we designed MultiLayer Perceptron (MLP) and Convolutional Neural Network
(CNN)-based models to identify target regions for ablation using a non-invasive
procedure, as BSP mapping, by dividing the atrial geometry into 7 regions where
the AF driver can be found. The main advantage of this methodology is to predict
those regions without using ECGI, which requires accurate mathematical modeling
of torso and atria. In the case of CNNs, where BSPs were converted into images, this
methodology has been demonstrated to be accurate and robust to noise. Moreover,
the proposed methodology makes transfer learning very easy to apply, since it can
be used to adapt much more complex pre-trained models to our specific task with
promising results. Finally, the proposed classification models can be easily extrapolated
to other atrial geometry divisions based on a higher number of smaller regions,
being able to get a higher resolution in the driver classification.
Descripción
Tesis Doctoral leída en la Universidad Rey Juan Carlos de Madrid en 2021. Directores de la Tesis: Óscar Barquero Pérez y
Eduardo Morgado Reyes
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