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Open AccessArticle

Symbolic Recurrence Analysis of RR Interval to Detect Atrial Fibrillation

Departamento de Tecnologías de la Información y las Comunicaciones, Campus la Muralla, Universidad Politécnica de Cartagena, Edif. Antigones, 30202 Cartagena, Spain
Departamento de Métodos Cuantitativos para la Economía y la Empresa, Campus de Espinardo, Universidad de Murcia, 30001 Murcia, Spain
Departamento de Cardiología, Hospital Universitario Virgen de la Arrixaca, 30120 Murcia, Spain
Unidad de Cuidados Intensivos, Hospital Universitario Los Arcos del Mar Menor, 30739 San Javier-Murcia, Spain
Departamento de Métodos Cuantitativos, Ciencias Jurídicas y Lenguas Modernas, Universidad Politécnica de Cartagena, Calle Real 3, Edif. CIM, 30202 Cartagena, Spain
Facultad de Ciencias Económicas y Empresariales, Universidad Nacional de Educación a Distancia, Paseo Senda del Rey, 11, 28040 Madrid, Spain
Icahn School of Medicine, 11 Mount Sinai, One Gustave L Levy Place, Box 1057, New York, NY 10029, USA
Author to whom correspondence should be addressed.
J. Clin. Med. 2019, 8(11), 1840;
Received: 27 September 2019 / Revised: 21 October 2019 / Accepted: 24 October 2019 / Published: 2 November 2019
(This article belongs to the Special Issue New Approaches to the Atrial Fibrillation Management)
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. View Full-Text
Keywords: atrial fibrillation; symbolic analysis; symbolic recurrence quantification analysis; logistic model atrial fibrillation; symbolic analysis; symbolic recurrence quantification analysis; logistic model
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Pérez-Valero, J.; Caballero Pintado, M.V.; Melgarejo, F.; García-Sánchez, A.-J.; Garcia-Haro, J.; García Córdoba, F.; García Córdoba, J.A.; 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. J. Clin. Med. 2019, 8, 1840.

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