79571 20210820090344.0 doi 10.1109/JBHI.2018.2851299 sideral 107422 ART-2018-107422 eng Martín Yebra, Alba Universidad de Zaragoza (orcid)0000-0003-0226-4950 Quantification of Ventricular Repolarization Variation for Sudden Cardiac Death Risk Stratification in Atrial Fibrillation 2018 Access copy available to the general public Unrestricted Objective: Atrial fibrillation (AF) rhythm gives rise to an irregular response in ventricular activity, preventing the use of standard ECG-derived risk markers based on ventricular repolarization heterogeneity under this particular condition. In this study we proposed new indices to quantify repolarization variations in AF patients, assessing their stratification performance in a chronic heart failure (CHF) population with AF. Methods: We developed a method based on a selective bin averaging technique. Consecutive beats preceded by a similar RR interval were selected, from which the average variation within the ST-T complex for each RR range was computed.We proposed two sets of indices: (i) the 2-beat index of ventricular repolarization variation, (I_V2), computed from pairs of stable consecutive beats; and (ii) the 3-beat indices of ventricular repolarization variation, computed in triplets of stable consecutive beats (I_V3). Results: These indices showed a significant association with sudden cardiac death (SCD) outcome in the study population. In addition, risk assessment based on the combination of the proposed indices improved stratification performance compared to their individual potential. Conclusion: Patients with enhanced ventricular repolarization variation computed in terms of the proposed indices were successfully associated to a higher SCD incidence in our study population, evidencing their prognostic value. Significance: using a simple ambulatory ECG recording, it is possible to stratify AF patients at risk of SCD, which may help cardiologists in adopting most effective therapeutic strategies, with a positive impact in both the patient and healthcare systems. info:eu-repo/grantAgreement/ES/DGA-FEDER/T39-17R-BSICoS info:eu-repo/grantAgreement/ES/MINECO/DPI2016-75458-R info:eu-repo/semantics/openAccess by http://creativecommons.org/licenses/by/3.0/es/ 4.217 2018 COMPUTER SCIENCE, INFORMATION SYSTEMS 19 / 155 = 0.123 2018 Q1 T1 MATHEMATICAL & COMPUTATIONAL BIOLOGY 5 / 59 = 0.085 2018 Q1 T1 MEDICAL INFORMATICS 4 / 26 = 0.154 2018 Q1 T1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS 16 / 106 = 0.151 2018 Q1 T1 1.122 2018 Biotechnology 2018 Q1 Health Information Management 2018 Q1 Electrical and Electronic Engineering 2018 Q1 Computer Science Applications 2018 Q1 info:eu-repo/semantics/article info:eu-repo/semantics/submittedVersion Laguna, Pablo Universidad de Zaragoza (orcid)0000-0003-3434-9254 Cygankiewicz, Iwona Bayés-de-Luna, Antoni Caiani, Enrico G. Martínez, Juan Pablo Universidad de Zaragoza (orcid)0000-0002-7503-3339 5008 800 Universidad de Zaragoza Dpto. Ingeniería Electrón.Com. Área Teoría Señal y Comunicac. 23, 3 (2018), 1049 - 1057 IEEE j. biomed. health inform. IEEE journal of biomedical and health informatics 2168-2194 398866 http://zaguan.unizar.es/record/79571/files/texto_completo.pdf Preprint 147385 http://zaguan.unizar.es/record/79571/files/texto_completo.jpg?subformat=icon icon Preprint oai:zaguan.unizar.es:79571 articulos driver 2021-08-20-08:37:06 ARTICLE