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  • Title: Gaussian modeling of the P-wave morphology time course applied to anticipate paroxysmal atrial fibrillation.
    Author: Martínez A, Alcaraz R, Rieta JJ.
    Journal: Comput Methods Biomech Biomed Engin; 2015; 18(16):1775-84. PubMed ID: 25298113.
    Abstract:
    This paper introduces a new algorithm to quantify the P-wave morphology time course with the aim of anticipating as much as possible the onset of paroxysmal atrial fibrillation (PAF). The method is based on modeling each P-wave with a single Gaussian function and analyzing the extracted parameters variability over time. The selected Gaussian approaches are associated with the amplitude, peak timing, and width of the P-wave. In order to validate the algorithm, electrocardiogram segments 2 h preceding the onset of PAF episodes from 46 different patients were assessed. According to the expected intermittently disturbed atrial conduction before the onset of PAF, all the analyzed Gaussian metrics showed an increasing variability trend as the PAF onset approximated. Moreover, the Gaussian P-wave width reported a diagnostic accuracy around 80% to discern between healthy subjects, patients far from PAF, and patients less than 1 h close to a PAF episode. This discriminant power was similar to those provided by the most classical time-domain approach, i.e., the P-wave duration. However, this newly proposed parameter presents the advantage of being less sensitive to a precise delineation of the P-wave boundaries. Furthermore, the linear combination of both metrics improved the diagnostic accuracy up to 86.69%. In conclusion, morphological P-wave characterization provides additional information to the metrics based on P-wave timing.
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