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

  • Nollo G
  • Marconcini M
  • Faes L
 et al. 
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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.

Author-supplied keywords

  • Arrhythmia organization
  • Automatic classification
  • Feature extraction and selection
  • Human atrial fibrillation
  • Intracardiac electrograms
  • Signal processing
  • Support vector machines (SVMs)

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  • Giandomenico Nollo

  • Mattia Marconcini

  • Francesca Bovolo

  • Flavia Ravelli

  • Lorenzo Bruzzone

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