On the Beat Detection Performance in Long-Term ECG Monitoring Scenarios

Sensors. 2018;18(5):1387 DOI 10.3390/s18051387

 

Journal Homepage

Journal Title: Sensors

ISSN: 1424-8220 (Print)

Publisher: MDPI AG

LCC Subject Category: Technology: Chemical technology

Country of publisher: Switzerland

Language of fulltext: English

Full-text formats available: PDF, HTML, XML

 

AUTHORS

Francisco-Manuel Melgarejo-Meseguer (Cardiology Service, Arrhythmia Unit, Hospital General Universitario Virgen de la Arrixaca, El Palmar, 30120 Murcia, Spain)
Estrella Everss-Villalba (Cardiology Service, Arrhythmia Unit, Hospital General Universitario Virgen de la Arrixaca, El Palmar, 30120 Murcia, Spain)
Francisco-Javier Gimeno-Blanes (Department of Signal Theory and Communications, Miguel Hernández University, Elche, 03202 Alicante, Spain)
Manuel Blanco-Velasco (Department of Signal Theory and Communications, University of Alcalá, Alcalá de Henares, 28805 Madrid, Spain)
Zaida Molins-Bordallo (Cardiology Service, Arrhythmia Unit, Hospital General Universitario Virgen de la Arrixaca, El Palmar, 30120 Murcia, Spain)
José-Antonio Flores-Yepes (Department of Signal Theory and Communications, Miguel Hernández University, Elche, 03202 Alicante, Spain)
José-Luis Rojo-Álvarez (Center for Computational Simulation, Universidad Politécnica de Madrid, Boadilla, 28223 Madrid, Spain)
Arcadi García-Alberola (Cardiology Service, Arrhythmia Unit, Hospital General Universitario Virgen de la Arrixaca, El Palmar, 30120 Murcia, Spain)

EDITORIAL INFORMATION

Blind peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 11 weeks

 

Abstract | Full Text

Despite the wide literature on R-wave detection algorithms for ECG Holter recordings, the long-term monitoring applications are bringing new requirements, and it is not clear that the existing methods can be straightforwardly used in those scenarios. Our aim in this work was twofold: First, we scrutinized the scope and limitations of existing methods for Holter monitoring when moving to long-term monitoring; Second, we proposed and benchmarked a beat detection method with adequate accuracy and usefulness in long-term scenarios. A longitudinal study was made with the most widely used waveform analysis algorithms, which allowed us to tune the free parameters of the required blocks, and a transversal study analyzed how these parameters change when moving to different databases. With all the above, the extension to long-term monitoring in a database of 7-day Holter monitoring was proposed and analyzed, by using an optimized simultaneous-multilead processing. We considered both own and public databases. In this new scenario, the noise-avoid mechanisms are more important due to the amount of noise that exists in these recordings, moreover, the computational efficiency is a key parameter in order to export the algorithm to the clinical practice. The method based on a Polling function outperformed the others in terms of accuracy and computational efficiency, yielding 99.48% sensitivity, 99.54% specificity, 99.69% positive predictive value, 99.46% accuracy, and 0.85% error for MIT-BIH arrhythmia database. We conclude that the method can be used in long-term Holter monitoring systems.