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P-wave characteristics after electrical external cardioversion: predictive indexes of relapse.

作者信息

Censi Federica, Calcagnini Giovanni, Triventi Michele, Mattei Eugenio, Bartolini Pietro, Corazza Ivan, Boriani Giuseppe

机构信息

Italian National Institute of Health, Viale Regina Elena 299, 00161 Roma, Italy.

出版信息

Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:3442-5. doi: 10.1109/IEMBS.2010.5627862.

Abstract

Atrial fibrillation (AF) is the most common arrhythmia in the western countries and accounts for hundred thousand strokes per year. Electrocardiographic characteristics of AF have been demonstrated to help identify patients at risk of developing AF. Prolonged and highly fragmented P-waves have been observed in patients prone to AF, and time-domain. Morphological characteristics of the P-wave from surface ECG recordings turned out to significantly distinguish patients at risk of AF. The aim of this study is to evaluate the morphological and time-domain characteristics of the P-wave in patients with AF relapse after cardioversion, respect to patients without. 14 patients who underwent successful electrical cardioversion for persistent AF were enrolled. Five minute ECG recordings were performed for each subject, immediately post-successful cardioversion. ECG signals were acquired by using a 16-lead mapping system for high-resolution biopotential measurements (sample frequency 2 kHz, 31 nV resolution, 0-400 Hz bandwidth). From the 16 recordings, a standard 12-lead ECG was derived and analyzed in terms of signal-averaged P-wave. Time-domain and mor-phological characteristics were estimated from the averaged P-waves of each lead. Time-domain features were quantified as: maximum P-wave duration in any of the 12 leads (Pmax), minimum P-wave duration in any of the leads (Pmin), P-wave dispersion (Pdisp=Pmax-Pmin), and Pindex (standard devia-tion of P-wave duration in any of the 12 leads). Morphological characteristics were extracted from a Gaussian function-based model of the P-wave as: average model order (Nav), maximum number of zero-crossing (PCmax), and maximum and average number of maxima and minima (FCImax and FCIav) in any of the leads. The results obtained so far indicate that the morphological and time-domain characteristics distinguish between patients with AF relapse and patients without.

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