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

Cardiology Service, Arrhythmia Unit, Hospital General Universitario Virgen de la Arrixaca, El Palmar, 30120 Murcia, Spain
Department of Signal Theory and Communications, Miguel Hernández University, Elche, 03202 Alicante, Spain
Department of Signal Theory and Communications, University of Alcalá, Alcalá de Henares, 28805 Madrid, Spain
Center for Computational Simulation, Universidad Politécnica de Madrid, Boadilla, 28223 Madrid, Spain
Department of Signal Theory and Communications, Rey Juan Carlos University, Fuenlabrada, 28943 Madrid, Spain
Authors to whom correspondence should be addressed.
Sensors 2018, 18(5), 1387;
Received: 26 March 2018 / Revised: 27 April 2018 / Accepted: 28 April 2018 / Published: 1 May 2018
(This article belongs to the Special Issue Sensors for Health Monitoring and Disease Diagnosis)
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. View Full-Text
Keywords: QRS detection; ECG; long-term monitoring; Holter; 7-day QRS detection; ECG; long-term monitoring; Holter; 7-day
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MDPI and ACS Style

Melgarejo-Meseguer, F.-M.; Everss-Villalba, E.; Gimeno-Blanes, F.-J.; Blanco-Velasco, M.; Molins-Bordallo, Z.; Flores-Yepes, J.-A.; Rojo-Álvarez, J.-L.; García-Alberola, A. On the Beat Detection Performance in Long-Term ECG Monitoring Scenarios. Sensors 2018, 18, 1387.

AMA Style

Melgarejo-Meseguer F-M, Everss-Villalba E, Gimeno-Blanes F-J, Blanco-Velasco M, Molins-Bordallo Z, Flores-Yepes J-A, Rojo-Álvarez J-L, García-Alberola A. On the Beat Detection Performance in Long-Term ECG Monitoring Scenarios. Sensors. 2018; 18(5):1387.

Chicago/Turabian Style

Melgarejo-Meseguer, Francisco-Manuel, Estrella Everss-Villalba, Francisco-Javier Gimeno-Blanes, Manuel Blanco-Velasco, Zaida Molins-Bordallo, José-Antonio Flores-Yepes, José-Luis Rojo-Álvarez, and Arcadi García-Alberola. 2018. "On the Beat Detection Performance in Long-Term ECG Monitoring Scenarios" Sensors 18, no. 5: 1387.

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