Literature DB >> 11954710

Automatic P-wave analysis of patients prone to atrial fibrillation.

L Clavier1, J M Boucher, R Lepage, J J Blanc, J C Cornily.   

Abstract

A method is presented for automatic analysis of the P-wave, based on lead II of a 12-lead standard ECG, in resting conditions during a routine examination for the detection of patients prone to atrial fibrillation (AF), one of the most prevalent arrhythmias. First, the P-wave was delineated, and this was achieved in two steps: the detection of the QRS complexes for ECG segmentation, using a wavelet analysis method, and a hidden Markov model to represent one beat of the signal for P-wave isolation. Then, a set of parameters to detect patients prone to AF was calculated from the P-wave. The detection efficiency was validated on an ECG database of 145 patients, including a control group of 63 people and a study group of 82 patients with documented AF. A discriminant analysis was applied, and the results obtained showed a specificity and a sensitivity between 65% and 70%.

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Year:  2002        PMID: 11954710     DOI: 10.1007/bf02347697

Source DB:  PubMed          Journal:  Med Biol Eng Comput        ISSN: 0140-0118            Impact factor:   2.602


  9 in total

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Journal:  IEEE Trans Biomed Eng       Date:  2000-07       Impact factor: 4.538

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Journal:  IEEE Trans Biomed Eng       Date:  1991-08       Impact factor: 4.538

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Authors:  C Li; C Zheng; C Tai
Journal:  IEEE Trans Biomed Eng       Date:  1995-01       Impact factor: 4.538

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Journal:  IEEE Trans Biomed Eng       Date:  1985-03       Impact factor: 4.538

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Authors:  W B Kannel; R D Abbott; D D Savage; P M McNamara
Journal:  N Engl J Med       Date:  1982-04-29       Impact factor: 91.245

7.  Detection of patients at risk for paroxysmal atrial fibrillation during sinus rhythm by P wave-triggered signal-averaged electrocardiogram.

Authors:  M Fukunami; T Yamada; M Ohmori; K Kumagai; K Umemoto; A Sakai; N Kondoh; T Minamino; N Hoki
Journal:  Circulation       Date:  1991-01       Impact factor: 29.690

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Authors:  P J Stafford; I Turner; R Vincent
Journal:  Am J Cardiol       Date:  1991-09-15       Impact factor: 2.778

9.  Atrial late potentials in patients with paroxysmal atrial fibrillation detected using a high gain, signal-averaged esophageal lead.

Authors:  G Q Villani; M Piepoli; T Cripps; A Rosi; U Gazzola
Journal:  Pacing Clin Electrophysiol       Date:  1994-06       Impact factor: 1.976

  9 in total
  4 in total

Review 1.  Computational modeling of the human atrial anatomy and electrophysiology.

Authors:  Olaf Dössel; Martin W Krueger; Frank M Weber; Mathias Wilhelms; Gunnar Seemann
Journal:  Med Biol Eng Comput       Date:  2012-06-21       Impact factor: 2.602

2.  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

3.  Atrial Fibrillation Detection During Sepsis: Study on MIMIC III ICU Data.

Authors:  Syed Khairul Bashar; Md Billal Hossain; Eric Ding; Allan J Walkey; David D McManus; Ki H Chon
Journal:  IEEE J Biomed Health Inform       Date:  2020-11-06       Impact factor: 7.021

4.  Automatic Prediction of Atrial Fibrillation Based on Convolutional Neural Network Using a Short-term Normal Electrocardiogram Signal.

Authors:  Urtnasan Erdenebayar; Hyeonggon Kim; Jong-Uk Park; Dongwon Kang; Kyoung-Joung Lee
Journal:  J Korean Med Sci       Date:  2019-02-15       Impact factor: 2.153

  4 in total

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