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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

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.

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    Date Created
    • 2012
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  • Text
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    • Partial requirement for: Ph.D., Arizona State University, 2012
      Note type
      thesis
    • Includes bibliographical references (p. 98-108)
      Note type
      bibliography
    • Field of study: Electrical engineering

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    by Shwetha Reddy Edla

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