6533b854fe1ef96bd12af452
RESEARCH PRODUCT
Non-invasive localization of atrial ectopic beats by using simulated body surface P-wave integral maps
Eduardo J GodoyMiguel LozanoLaura MartínezAna Ferrer-alberoRafael SebastianJavier SaizFelipe Atienzasubject
TachycardiaPhysiologyComputer sciencemedicine.medical_treatment02 engineering and technology030204 cardiovascular system & hematologyBioinformaticsBiochemistryACTIVATIONElectrocardiography0302 clinical medicineHeart RateAtrial FibrillationMedicine and Health SciencesImage Processing Computer-AssistedDEPOLARIZATIONBody surface P-wave integral mapsCardiac AtriaAtrial ectopic beatsMultidisciplinarymedicine.diagnostic_testORIGINApplied MathematicsSimulation and ModelingP waveBody Surface Potential MappingQRHeartHUMANSaarhythmiasAblationANATOMYBioassays and Physiological Analysismachine learningPhysical SciencesAtrial ectopic beatsMedicineAtrial Premature ComplexesFIBRILLATIONmedicine.symptomTACHYCARDIAAlgorithmsResearch ArticleclusteringTachycardia Ectopic AtrialComputer and Information SciencesSVMScienceCORONARY-SINUS0206 medical engineeringCardiologyResearch and Analysis MethodsMembrane PotentialTECNOLOGIA ELECTRONICAMachine Learning Algorithms03 medical and health sciencesArtificial IntelligenceHeart Conduction SystemSupport Vector MachinesBody surfacemedicineComputer SimulationHeart AtriaCoronary sinusFibrillationbusiness.industryElectrophysiological TechniquesBiology and Life SciencesPattern recognitionAtrial arrhythmiasELECTROPHYSIOLOGY020601 biomedical engineeringMODELElectrophysiologyCardiovascular AnatomyCardiac ElectrophysiologyArtificial intelligencebusinessElectrocardiographyBiomarkersMathematicsdescription
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2017-07-13 |