dc.contributor.author | Zalabarria Pena, Unai | |
dc.contributor.author | Irigoyen Gordo, Eloy | |
dc.contributor.author | Martínez Rodríguez, Raquel ![ORCID](/themes/Mirage2//images/orcid_16x16.png) | |
dc.contributor.author | Lowe, Andrew | |
dc.date.accessioned | 2024-02-08T10:38:20Z | |
dc.date.available | 2024-02-08T10:38:20Z | |
dc.date.issued | 2019-11-01 | |
dc.identifier.citation | Applied Mathematics and Computation 369 : (2020) // Article ID 124839 | |
dc.identifier.issn | 0096-3003 | |
dc.identifier.issn | 1873-5649 | |
dc.identifier.uri | http://hdl.handle.net/10810/65350 | |
dc.description.abstract | Nowadays, many contributions deal with R-peak detection in Electrocardiographic (ECG) signals. Although they present an accurate performance in detection, most of these are presented as offline solutions, both to be processed in high performance platforms (un- der a big cost), or to be analyzed in laboratories without constraints in time, neither in computational load. Owing to this, it is also very important to take one step further, try- ing to develop new solutions which work in portable/wearable low-cost platforms, with constraints in time and in computational load. In this work, an accurate and computationally efficient method for online and robust detection of R-Peaks is presented. This method is divided in three main stages: first, in the pre-processing stage, a complete elimination of artifacts is performed based on a noise and signal intensity approach; second, R-peaks detection is carried out through an effi- cient “area over the curve”method; finally, in the third stage, a novel iterative algorithm consisting in three sequential state machines performs the correct detection of the R-peaks applying heart period distance rules. Moreover, the method is performed over time in short length sliding windows. The algorithm has been tested using all 48 full-length ECG records of the MIT-BIH Ar- rhythmia Database, achieving 99.54% sensitivity and 99.60% positive predictivity in R-peak detection. | es_ES |
dc.description.sponsorship | This work has been performed thanks to the support of the University of the Basque Country (UPV/EHU), the In-
telligent Control Research Group of the UPV/EHU, the Pacific Atlantic Network for Technical Higher Education and Re-
search (PANTHER) program and the Institute of Biomedical Technologies (IBTec) of the Auckland University of Technology
https://doi.org/10.13039/10 0 0 08205, to which the authors are very grateful | |
dc.language.iso | eng | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.subject | electrocardiogram | es_ES |
dc.subject | ECG processing | es_ES |
dc.subject | R-peak detection | es_ES |
dc.subject | filtering | es_ES |
dc.subject | smart computing | es_ES |
dc.subject | state machine | es_ES |
dc.title | Online robust R-peaks detection in noisy electrocardiograms using a novel iterative smart processing algorithm | es_ES |
dc.type | info:eu-repo/semantics/preprint | es_ES |
dc.rights.holder | © 2019 Elsevier Inc. All rights reserved. | * |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S0096300319308318 | |
dc.identifier.doi | 10.1016/j.amc.2019.124839 | |
dc.departamentoes | Ingeniería de sistemas y automática | es_ES |
dc.departamentoeu | Sistemen ingeniaritza eta automatika | es_ES |