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Adaptive Parameter Estimation, Modeling and Patient-Specific Classification of Electrocardiogram Signals

Abstract Adaptive processing and classification of electrocardiogram (ECG) signals are important in eliminating the strenuous process of manually annotating ECG recordings for clinical use. Such algorithms require robust models whose parameters can adequately describe the ECG signals. Although different dynamic statistical models describing ECG signals currently exist, they depend considerably on a priori information and user-specified model parameters. Also, ECG beat morphologies, which vary greatly across patients and disease states, cannot be uniquely characterized by a single model. In this work, sequential Bayesian based methods are used to appropriately model and adaptively select the corresponding model parameters of ECG signals. An adaptive ... (more)
Created Date 2012
Contributor Edla, Shwetha Reddy (Author) / Papandreou-Suppappola, Antonia (Advisor) / Chakrabarti, Chaitali (Committee member) / Kovvali, Narayan (Committee member) / Tepedelenlioglu, Cihan (Committee member) / Arizona State University (Publisher)
Subject Electrical engineering / Adaptive parameter estimation / Bayesian methods / Classification / Electrocardiogram signals / Modeling / Patient-specific
Type Doctoral Dissertation
Extent 120 pages
Language English
Reuse Permissions All Rights Reserved
Note Ph.D. Electrical Engineering 2012
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis