Characterization of Atrial Repolarization Alternans and Activation Time Kinetics based on Intracardiac Electrograms

Florian Jousset
EPFL, 2011
Thèse EPFL

Résumé

Interdisciplinary collaboration between physicians and engineers is now widespread in fundamental and clinical studies, due to the increasing complexity of the physiological problems tackled, and the development of novel measurement devices and data analysis techniques. The present work is a clear illustration of this fact. In the study that triggered it, elaborate experiments on an animal model yielded large amounts of data perturbed by unavoidable interferences, and the phenomenon of interest had an elusive nature. This thesis describes the physiological motivation for this study, introduces the signal processing techniques developed to remove interferences and extract the parameters of interest, and proposes an interpretation of the results obtained. The present thesis is aimed at providing new insights into the mechanisms promoting atrial fibrillation during rapid pacing. Atrial fibrillation is the most common arrhythmia in the developed world, affecting millions of individuals. Atrial fibrillation initiates when triggers such as pulmonary vein tachycardias interact with substrates. However, the exact nature of the electrophysiological substrates that favor transition from runs of pulmonary veins tachycardia to persistent atrial fibrillation remains unclear. Interestingly, repolarization alternans, a beat-to-beat alternation in action potential duration or amplitude, has been mechanistically implicated in transitions from rapid pacing to atrial fibrillation by facilitating dispersion of atrial refractory periods. During rapid pacing, however, transitions from 1:1 to 2:1 atrial capture may supposedly antagonized the dispersion of repolarization driven by repolarization alternans. The ability of atrial unipolar depolarization and repolarization parameters (i.e. activation time and repolarization alternans) to predict the imminence of atrial fibrillation initiation by rapid pacing was evaluated at baseline (i.e. before any burst-pacing induced remodeling occurred). A chronic free-behaving ovine pacing model was developed to study the interplay between atrial repolarization alternans that may facilitate atrial fibrillation initiation during rapid pacing, and the onset of reduced excitability that may cause atrial capture failure and prevent atrial fibrillation initiation. We observed in human atria using monophasic action potential recordings transient decrease in excitability (as exemplified by activation time prolongation) and capture failure that appeared to quench repolarization alternans driven by rapid pacing. Using our ovine model of rapid pacing, human observations were successfully reproduced, which allowed us to analyze repolarization alternans and activation time kinetics until the first beat of capture failure. Prolongation of activation time suggestive of decreased excitability and repolarization alternans preceded 90% of the transitions from 1:1 to 2:1 atrial capture during rapid pacing. Notably, 2:1 atrial capture was imminent at pacing cycle lengths

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https://infoscience.epfl.ch/record/162108?ln=fr

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Florian Jousset
EPFL, 2011
Thèse EPFL

Résumé

Official source

https://infoscience.epfl.ch/record/162108?ln=fr

À propos de ce résultat

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Concepts associés

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Publications associées (4)

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Publications associées (4)

Statistical limits of high-dimensional inference problems

Clément Dominique Luneau

This thesis focuses on two kinds of statistical inference problems in signal processing and data science. The first problem is the estimation of a structured informative tensor from the observation of a noisy tensor in which it is buried. The structure comes from the possibility to decompose the informative tensor as the sum of a small number of rank-one tensors (small compared to its size). Such structure has applications in data science where data, organized into arrays, can often be explained by the interaction between a few features characteristic of the problem. The second problem is the estimation of a signal input to a feedforward neural network whose output is observed. It is relevant for many applications (phase retrieval, quantized signals) where the relation between the measurements and the quantities of interest is not linear. We look at these two statistical models in different high-dimensional limits corresponding to situations where the amount of observations and size of the signal become infinitely large. These asymptotic regimes are motivated by the ever-increasing computational power and storage capacity that make possible the processing and handling of large data sets. We take an information-theoretic approach in order to establish fundamental statistical limits of estimation in high-dimensional regimes. In particular, the main contributions of this thesis are the proofs of exact formulas for the asymptotic normalized mutual information associated with these inference problems. These are low-dimensional variational formulas that can nonetheless capture the behavior of a large system where each component interacts with all the others. Owing to the relationship between mutual information and Bayesian inference, we use the solutions to these variational problems to rigorously predict the asymptotic minimum mean-square error (MMSE), the error achieved by the (Bayes) optimal estimator. We can thus compare algorithmic performances to the statistical limit given by the MMSE. Variational formulas for the mutual information are referred to as replica symmetric (RS) ansätze due to the predictions of the heuristic replica method from statistical physics. In the past decade proofs of the validity of these predictions started to emerge. The general strategy is to show that the RS ansatz is both an upper and lower bound on the asymptotic normalized mutual information. The present work leverages on the adaptive interpolation method that proposes a unified way to prove the two bounds. We extend the adaptive interpolation to situations where the order parameter of the problem is not a scalar but a matrix, and to high-dimensional regimes that differ from the one for which the RS formula is usually conjectured. Our proofs also demonstrate the modularity of the method. Indeed, using statistical models previously studied in the literature as building blocks of more complex ones (e.g., estimated signal with a model of structure), we derive RS formulas for the normalized mutual information associated with estimation problems that are relevant to modern applications.

EPFL2021

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Acoustic tomography aims at recovering the unknown parameters that describe a field of interest by studying the physical characteristics of sound propagating through the considered field. The tomographic approach is appealing in that it is non-invasive and allows to obtain a significantly larger amount of data compared to the classical one-sensor one-measurement setup. It has, however, two major drawbacks which may limit its applicability in a practical setting: the methods by which the tomographic data are acquired and then converted to the field values are computationally intensive and often ill-conditioned. This thesis specifically addresses these two shortcomings by proposing novel acoustic tomography algorithms for signal acquisition and field reconstruction. The first part of our exposition deals with some theoretical aspects of the tomographic sampling problems and associated reconstruction schemes for scalar and vector tomography. We show that the classical time-of-flight measurements are not sufficient for full vector field reconstruction. As a solution, an additional set of measurements is proposed. The main advantage of the proposed set is that it can be directly computed from acoustic measurements. It thus avoids the need for extra measuring devices. We then describe three novel reconstruction methods that are conceptually quite different. The first one is based on quadratic optimization and does not require any a priori information. The second method builds upon the notion of sparsity in order to increase the reconstruction accuracy when little data is available. The third approach views tomographic reconstruction as a parametric estimation problem and solves it using recent sampling results on non-bandlimited signals. The proposed methods are compared and their respective advantages are outlined. The second part of our work is dedicated to the application of the proposed algorithms to three practical problems: breast cancer detection, thermal therapy monitoring, and temperature monitoring in the atmosphere. We address the problem of breast cancer detection by computing a map of sound speed in breast tissue. A noteworthy contribution of this thesis is the development of a signal processing technique that significantly reduces the artifacts that arise in very inhomogeneous and absorbent tissue. Temperature monitoring during thermal therapies is then considered. We show how some of our algorithms allow for an increased spatial resolution and propose ways to reduce the computational complexity. Finally, we demonstrate the feasibility of tomographic temperature monitoring in the atmosphere using a custom-built laboratory-scale experiment. In particular, we discuss various practical aspects of time-of-flight measurement using cheap, off-the-shelf sensing devices.

EPFL2008

Chargement

Time- and Space-Efficient Spatial Data Analytics

Mirjana Pavlovic

Advances in data acquisition technologies and supercomputing for large-scale simulations have led to an exponential growth in the volume of spatial data. This growth is accompanied by an increase in data complexity, such as spatial density, but also by more varied data distributions. As data evolves, so do the needs of applications. Recently, we notice a shift from predefined to ad-hoc workloads, as a result of the recent data exploration trend among data-driven applications. At the same time, given the massive volume of data, it has become imperative to use computational and storage resources efficiently, where efficiency requirements typically vary across applications. In this thesis, we show that traditional spatial data management techniques underperform as data size and complexity increase: they waste both computational and storage resources. They are also inefficient in supporting ad-hoc workloads. To achieve time- and space-efficiency, we design spatial data management algorithms and storage layouts that leverage and adapt to data characteristics and workload access patterns. In particular, we revisit the design of spatial join algorithms, indexing techniques and point cloud data management solutions. First, we propose data-aware spatial joins that leverage and adapt to dataset characteristics to avoid wasting computational resources and achieve time-efficiency on non-uniform data distributions. GIPSY is designed to efficiently join two datasets with contrasting densities. GIPSY uses the sparser dataset to guide the join process and therefore, by leveraging dataset characteristics, selectively retrieves and joins only the data needed. TRANSFORMERS achieves robust performance and time-efficiency on non-uniform data distributions, by adapting to dataset characteristics. It detects local variations in distributions and adapts the join strategy and data layout to local dataset characteristics at run-time. We next introduce incremental indexing approaches that take into account workload access patterns. This way, they minimize the data-to-insight time and avoid unnecessary preprocessing costs, substantially accelerating the exploratory analysis of spatial data. Incremental indexes are built as a side-effect of query execution and only for the parts of the data queried. Space Odyssey is designed for exploratory analyses of multiple spatial datasets that reside on disk. It takes advantage of workload access patterns to incrementally index the datasets and optimize accesses to parts frequently queried together. QUASII supports spatial data exploration in main memory. QUASII reduces the data-to-insight time and curbs the cost of incremental indexing, by gradually and partially sorting the data, while simultaneously producing a data-oriented hierarchical structure. Finally, we propose a time- and space-efficient solution to storing and managing point cloud data in main memory column-store database management systems. Our approach leverages point cloud data properties to employ dictionary-based compression in the spatial data management domain and enhances it with indexing capabilities by using space-filling curves. The proposed scheme also represents a partitioning strategy. It is a middle ground between data- and space-oriented partitioning, accounting for the data distribution, while preserving the simplicity of grid-like structures.

EPFL2019

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Time- and Space-Efficient Spatial Data Analytics

Mirjana Pavlovic

EPFL2019

Statistical limits of high-dimensional inference problems

Clément Dominique Luneau

EPFL2021

EPFL2008