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dc.contributor.authorPérez Valero, Jesús 
dc.contributor.authorCaballero Pintado, M Victoria 
dc.contributor.authorMelgarejo Meseguer, Francisco 
dc.contributor.authorGarcía Sánchez, Antonio 
dc.contributor.authorGarcía Haro, Juan 
dc.contributor.authorGarcía Córdoba, Francisco 
dc.contributor.authorGarcía Córdoba, José Antonio 
dc.contributor.authorPinar, Eduardo 
dc.contributor.authorGarcía Alberola, Arcadio 
dc.contributor.authorMatilla García, Mariano 
dc.contributor.authorCurtin, Paul 
dc.contributor.authorArora, Manish 
dc.contributor.authorRuiz Marín, Manuel
dc.identifier.citationPérez-Valero J, Caballero Pintado MV, Melgarejo F, García-Sánchez A-J, Garcia-Haro J, García Córdoba F, García Córdoba JA, Pinar E, García Alberola A, Matilla-García M, Curtin P, Arora M, Ruiz Marín M. Symbolic Recurrence Analysis of RR Interval to Detect Atrial Fibrillation. Journal of Clinical Medicine. 2019; 8(11):1840.
dc.description.abstractAtrial fibrillation (AF) is a sustained cardiac arrhythmia associated with stroke, heart failure, and related health conditions. Though easily diagnosed upon presentation in a clinical setting, the transient and/or intermittent emergence of AF episodes present diagnostic and clinical monitoring challenges that would ideally be met with automated ambulatory monitoring and detection. Current approaches to address these needs, commonly available both in smartphone applications and dedicated technologies, combine electrocardiogram (ECG) sensors with predictive algorithms to detect AF. These methods typically require extensive preprocessing, preliminary signal analysis, and the integration of a wide and complex array of features for the detection of AF events, and are consequently vulnerable to over-fitting. In this paper, we introduce the application of symbolic recurrence quantification analysis (SRQA) for the study of ECG signals and detection of AF events, which requires minimal pre-processing and allows the construction of highly accurate predictive algorithms from relatively few features. In addition, this approach is robust against commonly-encountered signal processing challenges that are expected in ambulatory monitoring contexts, including noisy and non-stationary data. We demonstrate the application of this method to yield a highly accurate predictive algorithm, which at optimal threshold values is 97.9% sensitive, 97.6% specific, and 97.7% accurate in classifying AF signals. To confirm the robust generalizability of this approach, we further evaluated its performance in the implementation of a 10-fold cross-validation paradigm, yielding 97.4% accuracy. In sum, these findings emphasize the robust utility of SRQA for the analysis of ECG signals and detection of AF. To the best of our knowledge, the proposed model is the first to incorporate symbolic analysis for AF beat detection.es_ES
dc.description.sponsorshipThis research was funded by projects AIM, ref. TEC2016-76465-C2-1-R (AEI/FEDER, UE), e-DIVITA, ref.20509/PDC/18 (Proof of Concept, 2018) and it is the result of the activity performed under the program Groups of Excellence of the Region of Murcia (Spain), the Fundación Séneca, Science and Technology Agency of the region of Murcia project under grant 19884/GERM/15 and ATENTO, ref. 20889/PI/18. All remaining errors are our responsibility.es_ES
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.titleSymbolic Recurrence Analysis of RR Interval to Detect Atrial Fibrillationes_ES
dc.subjectAtrial fibrillationes_ES
dc.subjectSymbolic analysises_ES
dc.subjectSymbolic recurrence quantification analysises_ES
dc.subject.unesco3109.04 Medicina Internaes_ES
dc.subject.unesco1103 Lógica Generales_ES
dc.contributor.convenianteUniversidad Politécnica de Cartagenaes_ES
dc.contributor.convenianteUniversidad de Murciaes_ES

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Atribución-NoComercial-SinDerivadas 3.0 España
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