Symbolic Recurrence Analysis of RR Interval to Detect Atrial Fibrillation
Autor
Pérez Valero, Jesús; Caballero Pintado, M Victoria; Melgarejo Meseguer, Francisco; García Sánchez, Antonio Javier; García Haro, Juan; [et al.]Patrocinadores
This 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.Realizado en/con
Universidad Politécnica de Cartagena; Universidad de MurciaFecha de publicación
2019-11-02Editorial
MDPICita bibliográfica
Pérez-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. https://doi.org/10.3390/jcm8111840Revisión por pares
SiPalabras clave
Atrial fibrillationSymbolic analysis
Symbolic recurrence quantification analysis
Logisticmodel
Resumen
Atrial 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-pro ...
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