Please use this identifier to cite or link to this item: https://hdl.handle.net/11000/30601
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dc.contributor.authorMelgarejo Meseguer, Francisco Manuel-
dc.contributor.authorEVERSS, ESTRELLA-
dc.contributor.authorGimeno Blanes, Francisco Javier-
dc.contributor.authorBlanco-Velasco, Manuel-
dc.contributor.authorMolins Bordallo, Zaida-
dc.contributor.authorFlores Yepes, Jose Antonio-
dc.contributor.authorRojo-Álvarez, José Luis-
dc.contributor.authorGarcía-Alberola, Arcadi-
dc.contributor.otherDepartamentos de la UMH::Ingeniería de Comunicacioneses_ES
dc.date.accessioned2024-01-24T11:18:05Z-
dc.date.available2024-01-24T11:18:05Z-
dc.date.created2018-04-
dc.identifier.citationSensors Volume 18 Issue 5(2018)es_ES
dc.identifier.issn1424-8220-
dc.identifier.urihttps://hdl.handle.net/11000/30601-
dc.description.abstractDespite 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.es_ES
dc.formatapplication/pdfes_ES
dc.format.extent24es_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectQRS detectiones_ES
dc.subjectECGes_ES
dc.subjectlong-term monitoringes_ES
dc.subjectHolteres_ES
dc.subject7-dayes_ES
dc.titleOn the Beat Detection Performance in Long-Term ECG Monitoring Scenarioses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherversionhttps://doi.org/10.3390/s18051387es_ES
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