Literature DB >> 18684285

Time-domain and morphological analysis of the P-wave. Part I: Technical aspects for automatic quantification of P-wave features.

Federica Censi1, Chiara Ricci, Giovanni Calcagnini, Michele Triventi, Renato P Ricci, Massimo Santini, Pietro Bartolini.   

Abstract

INTRODUCTION: Time-domain and morphological analysis of P-wave from surface electrocardiogram has been extensively used to identify patients prone to atrial arrhythmias, especially atrial fibrillation (AF). However, since no standard procedure exists for P-wave preprocessing, standardization of cut-off values for P-wave duration and morphological features is difficult. This study is a methodological investigation of P-wave preprocessing procedures for automatic time-domain and morphological analysis.
METHODS: We compared, on simulated and real data, the P-wave template obtained applying three alignment algorithms with that obtained without alignment, in terms of template error, shift error, P-wave duration, and morphological parameters. We also proposed automatic algorithms for estimation of P-wave duration.
RESULTS: We found that alignment is necessary for a reliable extraction of P-wave template by the averaging procedure, in order to perform time-domain and morphological analysis. On simulated and real data, the error on P-wave duration can be as high as 30 ms on a template obtained without alignment; if alignment procedure is performed, the error on P-wave duration is negligible. Analogously, morphological features are correctly estimated only on a P-wave template obtained with P-waves alignment. We also found that the proposed algorithm for the automatic estimation of the P-wave duration gave reliable results.

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Mesh:

Year:  2008        PMID: 18684285     DOI: 10.1111/j.1540-8159.2008.01102.x

Source DB:  PubMed          Journal:  Pacing Clin Electrophysiol        ISSN: 0147-8389            Impact factor:   1.976


  3 in total

1.  The P Wave Time-Frequency Variability Reflects Atrial Conduction Defects before Paroxysmal Atrial Fibrillation.

Authors:  Raúl Alcaraz; Arturo Martínez; José J Rieta
Journal:  Ann Noninvasive Electrocardiol       Date:  2014-11-23       Impact factor: 1.468

2.  CNN-FWS: A Model for the Diagnosis of Normal and Abnormal ECG with Feature Adaptive.

Authors:  Junjiang Zhu; Jintao Lv; Dongdong Kong
Journal:  Entropy (Basel)       Date:  2022-03-28       Impact factor: 2.738

3.  P-wave Variability and Atrial Fibrillation.

Authors:  Federica Censi; Ivan Corazza; Elisa Reggiani; Giovanni Calcagnini; Eugenio Mattei; Michele Triventi; Giuseppe Boriani
Journal:  Sci Rep       Date:  2016-05-26       Impact factor: 4.379

  3 in total

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