Document Detail


P-wave morphology assessment by a gaussian functions-based model in atrial fibrillation patients.
MedLine Citation:
PMID:  17405373     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Aim of this study was to present a P-wave model, based on a linear combination of Gaussian functions, to quantify morphological aspects of P-wave in patients prone to atrial fibrillation (AF). Five-minute ECG recordings were performed in 25 patients with permanent dual chamber pacemakers. Patients were divided into high-risk and low-risk groups, including patients with and without AF episodes in the last 6 mo preceding the study, respectively. ECG signals were acquired using a 32-lead mapping system for high-resolution biopotential measurement (ActiveTwo, Biosemi, The Netherlands, sample frequency 2 kHz, 24-bit resolution). Up to 8 Gaussian models have been computed for each averaged P-wave extracted from every lead. The P-wave morphology was evaluated by extracting seven parameters. Classical time-domain parameters, based on P-wave duration estimation, have been also estimated. We found that the P-wave morphology can be effectively modeled by a linear combination of Gaussian functions. In addition, the combination of time-domain and morphological parameters extracted from the Gaussian function-based model of the P-wave improves the identification of patients having different risks of developing AF.
Authors:
Federica Censi; G Calcagnini; C Ricci; R P Ricci; M Santini; A Grammatico; P Bartolini
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Publication Detail:
Type:  Evaluation Studies; Journal Article    
Journal Detail:
Title:  IEEE transactions on bio-medical engineering     Volume:  54     ISSN:  0018-9294     ISO Abbreviation:  IEEE Trans Biomed Eng     Publication Date:  2007 Apr 
Date Detail:
Created Date:  2007-04-04     Completed Date:  2007-04-24     Revised Date:  2009-11-11    
Medline Journal Info:
Nlm Unique ID:  0012737     Medline TA:  IEEE Trans Biomed Eng     Country:  United States    
Other Details:
Languages:  eng     Pagination:  663-72     Citation Subset:  IM    
Affiliation:
Department of Technologies and Health, Istituto Superiore di Sanità, Rome 00161, Italy. censi@iss.it
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MeSH Terms
Descriptor/Qualifier:
Aged
Algorithms*
Artificial Intelligence
Atrial Fibrillation / diagnosis*,  physiopathology*
Computer Simulation
Diagnosis, Computer-Assisted / methods*
Electrocardiography / methods*
Female
Humans
Male
Models, Cardiovascular*
Models, Statistical
Normal Distribution
Pattern Recognition, Automated / methods*
Reproducibility of Results
Sensitivity and Specificity

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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