title: An automatic system for the analysis and classification of human atrial fibrillation patterns from intracardiac electrograms.
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.
authors: Biophysics and Biosignals Laboratory, Department of Physics, University of Trento, 38050 Trento, Italy. nollo@science.unitn.it
authors: Giandomenico
authors: G
authors: Nollo
authors: Mattia
authors: M
authors: Marconcini
authors: Luca
authors: L
authors: Faes
authors: Francesca
authors: F
authors: Bovolo
authors: Flavia
authors: F
authors: Ravelli
authors: Lorenzo
authors: L
authors: Bruzzone
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001281:Atrial Fibrillation / Q000175:diagnosis*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity