Ventricular and Atrial Activity Estimation Through Sparse ECG Signal Decompositions

This paper explores a novel approach for ventricular and atrial activities estimation in electrocardiogram (ECG) signals, based on sparse source separation. Sparse decompositions of ECG over signal-adapted multi-component dictionaries can lead to natural separation of its components. In this work, dictionaries of functions adapted to ventricular and atrial activities are respectively defined. Then, the weighted orthogonal matching pursuit algorithm is used to unmix the two components of ECG signals. Despite the simplicity of the approach, results are very promising, showing the capacity of the algorithm to generate realistic estimations of atrial and ventricular activities

Detection and measurement and of repolarisation features in atrial fibrillation and healthy subjects

Major cardiac organisations recommended U wave abnormalities should be reported during ECG interpretation. However, U waves cannot be measured in patients with atrial fibrillation (AF) due to the obscuring fibrillatory wave.The first aim of the research was to provide a validated algorithm to clean the ECGs of AF patients by removing the atrial fibrillatory waves so that the characteristics of ventricle repolarisation components, U and T waves, could be detected and measured accurately without fibrillatory wave contamination.Having established a validated algorithm to measure the waveform features, the second aim was to use this algorithm to investigate the effect of beat interval dependency on the repolarisation waves, especially U waves, during AF and to compare them to those in sinus rhythm (SR) of healthy subjects. The research could provide mechanistic insight into the origin of U waves since AF is unique in its rapidly changing ventricular beat intervals. The preceding beat interval has a direct impact on ventricular filling dynamics and hence also on mechano-electrical coupling, one of the leading hypotheses of U wave genesis.Algorithms were developed to remove the contaminating fibrillatory waves in AF recordings and to measure features of the ventricular repolarisation waves.The ventricular repolarisation features, U and T waves, are measurable and dependent on preceding beat interval in AF and SR. The beat interval dependency of repolarisation features, especially the U wave, supported the mechano-electrical hypothesis during AF and SR.The research provides tools to facilitate the detection and reporting of U waves and their abnormalities in AF patients and provides mechanistic insight into rate dependency of ventricular repolarisation features

Computational Cardiology: Improving Markers and Models to Stratify Patients with Heart Disease.

Heart disease is the leading cause of death around the world, claiming over 17 million lives each year (30% of all global deaths). The burden of heart disease can be attributed, in part, to the lack of clinically useful tools that can accurately stratify patients and match them to appropriate therapies. In this thesis, we explore the use of computation as a solution to this problem. Specifically, the goal of our work is to develop novel approaches that can be applied to cardiovascular datasets to discover diagnostic markers and to improve models for predicting adverse cardiovascular outcomes. Our research focuses on the following opportunities: (1) improving the computational efficiency of existing ECG markers while maintaining clinically useful discrimination; (2) developing new ECG markers based on short-term heart rate structure that are complementary to existing markers; (3) building more accurate models in the presence of small training cohorts with class-imbalance; and (4) proposing approaches to decompose ECG signals into atrial and ventricular components to predict arrhythmias arising from specific anatomical regions. When evaluated on multiple cohorts comprising patients with coronary artery disease and patients undergoing cardiothoracic surgery, our work substantially improves the ability to deliver cardiac care.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/108791/1/jazzchia_1.pd

Nonlinear approximation with redundant multi-component dictionaries

The problem of efficiently representing and approximating digital data is an open challenge and it is of paramount importance for many applications. This dissertation focuses on the approximation of natural signals as an organized combination of mutually connected elements, preserving and at the same time benefiting from their inherent structure. This is done by decomposing a signal onto a multi-component, redundant collection of functions (dictionary), built by the union of several subdictionaries, each of which is designed to capture a specific behavior of the signal. In this way, instead of representing signals as a superposition of sinusoids or wavelets many alternatives are available. In addition, since dictionaries we are interested in are overcomplete, the decomposition is non-unique. This gives us the possibility of adaptation, choosing among many possible representations the one which best fits our purposes. On the other hand, it also requires more complex approximation techniques whose theoretical decomposition capacity and computational load have to be carefully studied. In general, we aim at representing a signal with few and meaningful components. If we are able to represent a piece of information by using only few elements, it means that such elements can capture its main characteristics, allowing to compact the energy carried by a signal into the smallest number of terms. In such a framework, this work also proposes analysis methods which deal with the goal of considering the a priori information available when decomposing a structured signal. Indeed, a natural signal is not only an array of numbers, but an expression of a physical event about which we usually have a deep knowledge. Therefore, we claim that it is worth exploiting its structure, since it can be advantageous not only in helping the analysis process, but also in making the representation of such information more accessible and meaningful. The study of an adaptive image representation inspired and gave birth to this work. We often refer to images and visual information throughout the course of the dissertation. However, the proposed approximation setting extends to many different kinds of structured data and examples are given involving videos and electrocardiogram signals. An important part of this work is constituted by practical applications: first of all we provide very interesting results for image and video compression. Then, we also face the problem of signal denoising and, finally, promising achievements in the field of source separation are presented