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TIME- AND FREQUENCY-BASED INDEPENDENT EVALUATION OF QRST CANCELLATION TECHNIQUES FOR SINGLE-LEAD ELECTROCARDIOGRAMS DURING ATRIAL FIBRILLATION.
Price, Nicholas F; Berenfeld, Omer; Devabhaktuni, Vijay; Deo, Makarand.
Afiliación
  • Price NF; Department of Electrical and Computer Engineering, University of Toledo, Toledo, OH 43607 USA.
  • Berenfeld O; Center for Arrhythmia Research, University of Michigan, Ann Arbor, MI 48107 USA.
  • Devabhaktuni V; Department of Electrical and Computer Engineering, The University of Maine, Orono, MN 04469, USA.
  • Deo M; Department of Engineering, Norfolk State University, Norfolk, VA 23504 USA.
Annu Model Simul Conf ANNSIM ; 2022: 294-304, 2022 Jul.
Article en En | MEDLINE | ID: mdl-36745140
ABSTRACT
With the increased prevalence of atrial fibrillation (AF) - a rhythm disturbance in heart's top chambers - there is growing interest in accurate non-invasive diagnosis of atrial activity to improve its therapy. A key component in non-invasive analysis of atrial activity is a successful removal of the ventricular QRST complexes from electrocardiograms (ECGs). In this study, we have developed a new approach for an objective and physiologically-based evaluation of QRST cancellation methods based on comparisons with the power spectra of the AF. Three commonly used QRST cancellation methods were evaluated; namely, average beat subtraction, singular value cancellation, and principal component analysis. These methods were evaluated in time and frequency domains using a set of synthesized ECGs preserving the atrial-specific temporal and spectral properties. It was observed that the ABS method provided the best estimation when QRST morphological variability is low, while PCA produces an overall best estimate when a large QRST morphological variability is present.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Risk_factors_studies Idioma: En Revista: Annu Model Simul Conf ANNSIM Año: 2022 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Risk_factors_studies Idioma: En Revista: Annu Model Simul Conf ANNSIM Año: 2022 Tipo del documento: Article