An automatic system for the analysis and classification of human atrial fibrillation patterns from intracardiac electrograms

Luca Faes, Flavia Ravelli, Giandomenico Nollo, Lorenzo Bruzzone, Francesca Bovolo, Mattia Marconcini

Risultato della ricerca: Article

22 Citazioni (Scopus)

Abstract

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
Lingua originaleEnglish
pagine (da-a)2275-2285
Numero di pagine11
RivistaIEEE Transactions on Biomedical Engineering
Volume55
Stato di pubblicazionePublished - 2008

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Feature extraction
Set theory
Maximum likelihood
Support vector machines
Classifiers
Chemical activation

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering

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An automatic system for the analysis and classification of human atrial fibrillation patterns from intracardiac electrograms. / Faes, Luca; Ravelli, Flavia; Nollo, Giandomenico; Bruzzone, Lorenzo; Bovolo, Francesca; Marconcini, Mattia.

In: IEEE Transactions on Biomedical Engineering, Vol. 55, 2008, pag. 2275-2285.

Risultato della ricerca: Article

Faes, Luca ; Ravelli, Flavia ; Nollo, Giandomenico ; Bruzzone, Lorenzo ; Bovolo, Francesca ; Marconcini, Mattia. / An automatic system for the analysis and classification of human atrial fibrillation patterns from intracardiac electrograms. In: IEEE Transactions on Biomedical Engineering. 2008 ; Vol. 55. pagg. 2275-2285.
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abstract = "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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