Browsing by Subject "Signal Processing"

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    Signal processing approaches for the spatiotemporal analysis of cardiac arrhythmias using intracardiac electrograms
    (2022-02) Ravikumar, Vasanth
    Each heartbeat is controlled by an electrical wave of excitation that propagates throughthe heart and initiates cardiac contraction. The normal heartbeat is initiated by pacemaker cells in the sinus node located in the right atrium, propagate throughout the atria, and then enters the ventricles via the atrioventricular junction and finally ends in the Purkinje fibers. The rate and regularity of these cardiac rhythms are determined by the intrinsic firing rate (automaticity) of the pacemaker cells and the influence of extrinsic factors, including various ionic mechanisms and drugs. Abnormal regimes of wave initiation and propagation result in cardiac arrhythmias. Various mechanisms, including local ectopic activity, focal triggers, wave breaks, and functional reentry, drive the arrhythmic activity in the heart. The spatiotemporal complexity of each of these underlying mechanisms is different, with more complexity seen in tachyarrhythmias and less complexity for bradyarrhythmias. Understanding the spatiotemporal complexity of the different arrhythmias is of great interest to electrophysiologists. In recent years, catheter ablation therapy (non-pharmacological approach) has had anincreasingly important role in curing many arrhythmias. The underlying spatiotemporal complexity of each arrhythmia plays an important role in deciding the target sites for ablation in this therapy. Currently, existing signal analysis techniques are not robust for all types of arrhythmias. Therefore it is essential to develop new approaches that can fully capture the intrinsic dynamics and the spatiotemporal complexity of both atrial and ventricular arrhythmias using intracardiac electrogram signals. Some novel approaches, namely multiscale frequency, multiscale entropy, kurtosis, and Shannon entropy was developed using the ex-vivo optical mapping of rabbit hearts. But, the nature of signals obtained during optical mapping is very different from the intracardiac electrograms obtained during the catheter ablation procedure. Also, the clinical recordings suffer from limitations such as sparse spatial data availability and sequential mapping. Therefore it is essential to enhance the above techniques to work on the intracardiac electrograms and also identify the spatial sites in the heart that maintain these arrhythmic activities. For my study, the intracardiac analysis was performed under two different types ofcardiac arrhythmic rhythms, namely Atrial Fibrillation (AF) and Ventricular Fibrillation (VF). Atrial Fibrillation (AF) is an arrhythmia in the upper two chambers (atria) of the heart. AF is responsible for significant impairment in quality of life and contributes to substantial morbidity and health care expenditure. AF is the most common arrhythmia in humans and, as such, is heterogeneous in its mechanism, presentation, and clinical course and therefore requires individualized treatment. Ventricular fibrillation (VF) is a type of lethal heart rhythm. During ventricular fibrillation, disorganized heart signals cause the lower heart chambers (ventricles) to quiver, and the heart does not pump blood to the rest of the body. Ventricular fibrillation is an emergency that requires immediate medical attention. It's the most frequent cause of sudden cardiac death. Although both these rhythms originate at different locations of the heart and havedifferent types of rhythms and morphology, the underlying spatiotemporal organizations and intracardiac electrogram analysis approaches are similar. Therefore, my thesis consists of the following three objectives: 1. Clinical implementation and validation of novel approaches using intracardiac electrograms to characterize the spatiotemporal dynamics of the AF arrhythmic activities. 2. Development of a similarity score using a combination of various iEGMs analysis techniques to more accurately identify the spatial location of active sites in AF patients. 3. Development of an analytical approach to characterize the organization (organized or disorganized) of VF electrical activities using clinical intracardiac electrograms.
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    Solving Inverse Problems in Medical Imaging Using Statistical Signal Processing and Deep Learning
    (2020-02) Hosseini, Seyed Amir Hossein
    Solving inverse problems remain an active research area in various fields to study the cause of a phenomenon by observing the effects. In particular, such efforts are well grounded in medical imaging applications where inverse problems naturally arise due to the imaging target being either inaccessible or invisible to human eyes. Non-regularity or ill-conditioning is a major challenge in such situations which is a direct consequence of limited observations/measurements being available. Medical imaging applications have classically incorporated domain-specific knowledge about the forward encoding operator to regularize the pertaining inverse problem. Deep learning has recently received high interest as an alternative approach for data-driven regularization. In principle, such regularization can be achieved by acquiring more measurements. However, sufficient measurements are usually unavailable due to practical considerations. For instance, in electromagnetic source imaging (ESI) using electroencephalography (EEG), number of recording electrodes can not be excessively increased due to subsequent distortions that electrodes contacts can cause in the medium. This limitation adversely affects the spatial resolution of EEG-based ESI. Another example is magnetic resonance imaging (MRI) where increasing imaging resolution trades off with noise properties, temporal resolution and scan duration. High-resolution MRI can become prohibitively long especially when a sequence of images need to be obtained. Thus, accelerated MRI techniques are required to reconstruct high-resolution images using less data. The present thesis contributes to solving regularization issues pertaining to inverse problems in medical imaging with a focus on high-resolution electromagnetic source imaging (ESI) using electroencephalography (EEG) and accelerated high resolution magnetic resonance imaging (MRI) with high fidelity. The ESI spatial resolution and the brain full coverage is shown to significantly improve when simultaneous scalp EEG and intracranial EEG recordings are jointly utilized. Additionally, a new ESI technique is proposed that incorporates forward modeling uncertainties into inverse problem formulations, considerably improving localization accuracy and recovering underlying source dynamics and extents when such uncertainties are properly determined and modeled. A novel deep learning-based reconstruction technique is proposed for accelerated MRI using a recurrent neural network architecture that offers desired noise properties compared to conventional techniques, particularly at higher acceleration rates. Furthermore, a new deep learning methodology is developed that unrolls inverse problem optimization algorithms into dense recurrent neural networks for improved reconstruction performance without requiring extra computational power or increasing reconstruction time.