TY - JOUR A1 - Pérez Valero, Jesús AU - Caballero Pintado, M Victoria AU - Melgarejo Meseguer, Francisco AU - García Sánchez, Antonio Javier AU - García Haro, Juan AU - García Córdoba, Francisco AU - García Córdoba, José Antonio AU - Pinar, Eduardo AU - García Alberola, Arcadio AU - Matilla García, Mariano AU - Curtin, Paul AU - Arora, Manish AU - Ruiz Marín, Manuel T1 - Symbolic Recurrence Analysis of RR Interval to Detect Atrial Fibrillation Y1 - 2019 SN - 2077-0383 UR - http://hdl.handle.net/10317/9367 AB - 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-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. KW - Atrial fibrillation KW - Symbolic analysis KW - Symbolic recurrence quantification analysis KW - Logisticmodel KW - 3109.04 Medicina Interna KW - 1103 Lógica General LA - eng PB - MDPI ER -