| An automatic system for the analysis and classification of human atrial fibrillation patterns from intracardiac electrograms. | |
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MedLine Citation:
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PMID: 18713697 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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This paper presents an automatic system for the analysis and classification of atrial fibrillation (AF) patterns from bipolar intracardiac signals. The system is made up of: 1) a feature-extraction module that defines and extracts a set of measures potentially useful for characterizing AF types on the basis of their degree of organization; 2) a feature-selection module (based on the Jeffries-Matusita distance and a branch and bound search algorithm) identifying the best subset of features for discriminating different AF types; and 3) a support vector machine technique-based classification module that automatically discriminates the AF types according to the Wells' criteria. The automatic system was applied on 100 intracardiac AF signal strips and on a selection of 11 representative features, demonstrating: a) the possibility to properly identify the most significant features for the discrimination of AF types; b) higher accuracy (97.7% using the seven most informative features) than the traditional maximum likelihood classifier; and c) effectiveness in AF classification also with few training samples (accuracy = 88.3% with only five training signals). Finally, the system identifies a combination of indices characterizing changes of morphology of atrial activation waves and perturbation of the isoelectric line as the most effective in separating the AF types. |
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Authors:
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Giandomenico Nollo; Mattia Marconcini; Luca Faes; Francesca Bovolo; Flavia Ravelli; Lorenzo Bruzzone |
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Publication Detail:
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Type: Journal Article; Research Support, Non-U.S. Gov't |
Journal Detail:
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Title: IEEE transactions on bio-medical engineering Volume: 55 ISSN: 1558-2531 ISO Abbreviation: IEEE Trans Biomed Eng Publication Date: 2008 Sep |
Date Detail:
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Created Date: 2008-08-20 Completed Date: 2008-10-17 Revised Date: 2009-11-11 |
Medline Journal Info:
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Nlm Unique ID: 0012737 Medline TA: IEEE Trans Biomed Eng Country: United States |
Other Details:
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Languages: eng Pagination: 2275-85 Citation Subset: IM |
Affiliation:
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Biophysics and Biosignals Laboratory, Department of Physics, University of Trento, 38050 Trento, Italy. nollo@science.unitn.it |
Export Citation:
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| MeSH Terms | |
Descriptor/Qualifier:
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Algorithms* Artificial Intelligence* Atrial Fibrillation / diagnosis* Diagnosis, Computer-Assisted / methods* Humans Pattern Recognition, Automated / methods* Reproducibility of Results Sensitivity and Specificity |
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