Automatic quality electrogram assessment improves phase-based reentrant activity identification in atrial fibrillation. (February 2020)

• Record Type: Journal Article
• Authors: Costoya-Sánchez, Alejandro; Climent, Andreu M.; Hernández-Romero, Ismael; Liberos, Alejandro; Fernández-Avilés, Francisco; Narayan, Sanjiv M.; Atienza, Felipe; Guillem, Maria S.; Rodrigo, Miguel
• Is Part Of: Computers in biology and medicine. Volume 117(2020)
• Publication Date: 2020-02
• Subjects: Atrial fibrillation -- Driver -- Rotor -- Source -- Reentry -- Phase mapping -- Basket mapping; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2019.103593
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Identification of reentrant activity driving atrial fibrillation (AF) is increasingly important to ablative therapies. The goal of this work is to study how the automatically-classified quality of the electrograms (EGMs) affects reentrant AF driver localization. EGMs from 259 AF episodes obtained from 29 AF patients were recorded using 64-poles basket catheters and were manually classified according to their quality. An algorithm capable of identifying signal quality was developed using time and spectral domain parameters. Electrical reentries were identified in 3D phase maps using phase transform and were compared with those obtained with a 2D activation-based method. Effect of EGM quality was studied by discarding 3D phase reentries detected in regions with low-quality EGMs. Removal of reentries identified by 3D phase analysis in regions with low-quality EGMs improved its performance, increasing the area under the ROC curve (AUC) from 0.69 to 0.80. The EGMs quality classification algorithm showed an accurate performance for EGM classification (AUC 0.94) and reentry detection (AUC 0.80). Automatic classification of EGM quality based on time and spectral signal parameters is feasible and accurate, avoiding the manual labelling. Discard of reentries identified in regions with automatically-detected poor-quality EGMs improved the specificity of the 3D phase-based method for AF driver identification. Highlights: Identification of reentrant activity driving AF depends on signal quality and catheter deployment. Including the catheter 3D geometry benefits the AF driver identification. Signal quality estimator improves the performance of the 3D detection. Automated reentrant activity identification using 3D geometry and signal quality may improve ablation-guided procedures.