Non-invasive localization of atrial ectopic beats by using simulated body surface P-wave integral maps

• Published in: PloS one Vol. 12; no. 7; p. e0181263
• Main Authors: Ferrer-Albero, Ana, Godoy, Eduardo J, Lozano, Miguel, Martínez-Mateu, Laura, Atienza, Felipe, Saiz, Javier, Sebastian, Rafael
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 13-07-2017; Public Library of Science (PLoS)
• Subjects: Ablation; Algorithms; Anatomy & physiology; Artificial intelligence; Atrial ectopic beats; Atrial Fibrillation - physiopathology; Atrial Premature Complexes - diagnosis; Atrial Premature Complexes - physiopathology; Bioindicators; Biology and Life Sciences; Body Surface Potential Mapping - methods; Cardiac arrhythmia; Clusters; Computer and Information Sciences; Computer science; Computer Simulation; Diagnosis; Electric potential; Electrocardiography; Electrocardiography - methods; Heart Atria - pathology; Heart Atria - physiopathology; Heart Conduction System - physiopathology; Humans; Image Processing, Computer-Assisted; Inverse estimation; Learning algorithms; Localization; Machine learning; Medical imaging; Medicine and Health Sciences; Methods; Morphology; Physical Sciences; Physiological aspects; Physiology; Principal components analysis; Research and Analysis Methods; Studies; Tachycardia, Ectopic Atrial - diagnosis; Tachycardia, Ectopic Atrial - physiopathology
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0181263

Non-invasive localization of continuous atrial ectopic beats remains a cornerstone for the treatment of atrial arrhythmias. The lack of accurate tools to guide electrophysiologists leads to an increase in the recurrence rate of ablation procedures. Existing approaches are based on the analysis of the P-waves main characteristics and the forward body surface potential maps (BSPMs) or on the inverse estimation of the electric activity of the heart from those BSPMs. These methods have not provided an efficient and systematic tool to localize ectopic triggers. In this work, we propose the use of machine learning techniques to spatially cluster and classify ectopic atrial foci into clearly differentiated atrial regions by using the body surface P-wave integral map (BSPiM) as a biomarker. Our simulated results show that ectopic foci with similar BSPiM naturally cluster into differentiated non-intersected atrial regions and that new patterns could be correctly classified with an accuracy of 97% when considering 2 clusters and 96% for 4 clusters. Our results also suggest that an increase in the number of clusters is feasible at the cost of decreasing accuracy.