Feature subset selection and classification of intracardiac electrograms during atrial fibrillation

Several approaches have been adopted for the identification of arrhythmogenic sources maintaining atrial fibrillation (AF). In this paper, we propose a classifier that discriminates between four classes of atrial electrogram (EGM). We delved into the relation between levels of fractionation in EGM s...

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Autores:
Tipo de recurso:
Fecha de publicación:
2017
Institución:
Universidad de Medellín
Repositorio:
Repositorio UDEM
Idioma:
eng
OAI Identifier:
oai:repository.udem.edu.co:11407/4268
Acceso en línea:
http://hdl.handle.net/11407/4268
Palabra clave:
Atrial fibrillation
Electroanatomical mapping
Fractionated electrograms
K-NN classifier
Rotor
Ablation
Diseases
Feature extraction
Genetic algorithms
Nearest neighbor search
Rotors
Text processing
Atrial electrograms
Atrial fibrillation
Electrograms
Feature extraction methods
Feature subset selection
Intracardiac electrograms
k-NN classifier
Radio-frequency Ablation
Biomedical signal processing
Rights
License
http://purl.org/coar/access_right/c_16ec
Description
Summary:Several approaches have been adopted for the identification of arrhythmogenic sources maintaining atrial fibrillation (AF). In this paper, we propose a classifier that discriminates between four classes of atrial electrogram (EGM). We delved into the relation between levels of fractionation in EGM signals and the fibrillation substrates in simulated episodes of chronic AF. Several feature extraction methods were used to calculate 92 features from 429 real EGM records acquired during radiofrequency ablation of chronic AF. We selected the optimal subset of features by using a genetic algorithm, followed by K-nearest neighbors (K-NN) classification into four levels of fractionation. Sensitivity of 0.90 and specificity of 0.97 were achieved. Subsequently, the results of the classification were extrapolated to signals of a 3D human atria model and a 2D model of atrial tissue. The 3D model simulated an episode of AF maintained by a rotor in the posterior wall of the left atrium and the 2D model simulated an AF episode with one stable rotor. We used the K-NN classifier trained on a given set of real EGM signals to detect a specific class of signals presenting the highest level of fractionation located near the rotor's vortex. This method needs to be tested on real clinical data to provide evidence that it can support ablation therapy procedures. © 2017 Elsevier Ltd