6533b7ddfe1ef96bd1273fa3
RESEARCH PRODUCT
Recurrence quantification analysis as a tool for complex fractionated atrial electrogram discrimination
Nicolas NavoretGabriel LaurentStéphane BinczakSabir Jacquirsubject
medicine.medical_specialty[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing[ NLIN.NLIN-CD ] Nonlinear Sciences [physics]/Chaotic Dynamics [nlin.CD]0206 medical engineeringTreatment outcome[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing02 engineering and technology030204 cardiovascular system & hematologySensitivity and SpecificityIntracardiac injection03 medical and health sciences0302 clinical medicine[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing[SDV.MHEP.CSC]Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular systemInternal medicineAtrial FibrillationmedicineHumansDiagnosis Computer-AssistedRetrospective Studiesmedicine.diagnostic_testbusiness.industryBody Surface Potential MappingReproducibility of ResultsAtrial fibrillation[ SDV.MHEP.CSC ] Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular systemmedicine.disease020601 biomedical engineeringTreatment OutcomeSurgery Computer-AssistedRecurrence quantification analysis[NLIN.NLIN-CD]Nonlinear Sciences [physics]/Chaotic Dynamics [nlin.CD]Cardiologybusiness[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingElectrocardiographyAlgorithmsdescription
International audience; Atrial fibrillation is the most encountered pathology of the heart rate. The reasons of its occurrence and its particular characteristics remain unknown, resulting from complex phenomena interaction. From these interactions emerges Complex Fractionated Atrial Electrograms (CFAE) which are useful for the ablation procedure. This study presents a method based on nonlinear data analysis, the Recurrence Quantification Analysis (RQA) applied on intracardiac atrial electrograms to detect CFAE particularities. The results obtained on areas previously tagged by a cardilogist show a good sensitivity to CFAE. Combination of RQA features offers a larger discrimination potential for future automated detection.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2012-09-01 | 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society |