- -

Application of artificial neural networks for versatile preprocessing of electrocardiogram recordings

RiuNet: Institutional repository of the Polithecnic University of Valencia

Share/Send to

Cited by

Statistics

Application of artificial neural networks for versatile preprocessing of electrocardiogram recordings

Show simple item record

Files in this item

dc.contributor.author Mateo, Jorge es_ES
dc.contributor.author Rieta Ibañez, José Joaquín es_ES
dc.date.accessioned 2014-02-24T19:39:08Z
dc.date.issued 2012-02
dc.identifier.issn 0309-1902
dc.identifier.uri http://hdl.handle.net/10251/35927
dc.description.abstract [EN] The electrocardiogram (ECG) is the most widely used method for diagnosis of heart diseases, where a good quality of recordings allows the proper interpretation and identification of physiological and pathological phenomena. However, ECG recordings often have interference from noises including thermal, muscle, baseline and powerline noises. These signals severely limit ECG recording utility and, hence, have to be removed. To deal with this problem, the present paper proposes an artificial neural network (ANN) as a filter to remove all kinds of noise in just one step. The method is based on a growing ANN which optimizes both the number of nodes in the hidden layer and the coefficient matrices, which are optimized by means of the Widrow-Hoff delta algorithm. The ANN has been trained with a database comprising all kinds of noise, both from synthesized and real ECG recordings, in order to handle any noise signal present in the ECG. The proposed system improves results yielded by conventional techniques of ECG filtering, such as FIR-based systems, adaptive filtering and wavelet filtering. Therefore, the algorithm could serve as an effective framework to substantially reduce noise in ECG recordings. In addition, the resulting ECG signal distortion is notably more reduced in comparison with conventional methodologies. In summary, the current contribution introduces a new method which is able to suppress all ECG interference signals in only one step with low ECG distortion and a high noise reduction. es_ES
dc.description.sponsorship This work was partly sponsored by Universidad de Castilla-La Mancha, Patronato Universitario Cardenal Gil de Albornoz from Cuenca, the project TEC2010–20633 from the Spanish Ministry of Science and Innovation and PII2C09–0224–5983 and PII1C09–0036–3237 from Junta de Comunidades de Castilla La Mancha.
dc.format.extent 12 es_ES
dc.language Inglés es_ES
dc.publisher Informa Healthcare es_ES
dc.relation MICINN/TEC2010-20633 es_ES
dc.relation JCCM/PII2C09-0224-5983 es_ES
dc.relation JCCM/PII1C09-0036-3237
dc.relation.ispartof Journal of Medical Engineering and Technology es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Biomedical signals es_ES
dc.subject Muscle and baseline noise es_ES
dc.subject Powerline interference es_ES
dc.subject Electrocardiogram es_ES
dc.subject Artificial neural network es_ES
dc.subject.classification TECNOLOGIA ELECTRONICA es_ES
dc.title Application of artificial neural networks for versatile preprocessing of electrocardiogram recordings es_ES
dc.type Artículo es_ES
dc.embargo.lift 10000-01-01
dc.embargo.terms forever es_ES
dc.identifier.doi 10.3109/03091902.2011.636859
dc.rights.accessRights Cerrado es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Electrónica - Departament d'Enginyeria Electrònica es_ES
dc.contributor.affiliation Universitat Politècnica de València. Grupo de ingeniería en bioseñales e imagen radiológica es_ES
dc.description.bibliographicCitation Mateo, J.; Rieta Ibañez, JJ. (2012). Application of artificial neural networks for versatile preprocessing of electrocardiogram recordings. Journal of Medical Engineering and Technology. 36(2):90-101. doi:10.3109/03091902.2011.636859 es_ES
dc.description.accrualMethod Senia es_ES
dc.relation.publisherversion http://dx.doi.org/10.3109/03091902.2011.636859 es_ES
dc.description.upvformatpinicio 90 es_ES
dc.description.upvformatpfin 101 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 36 es_ES
dc.description.issue 2 es_ES
dc.relation.senia 239056
dc.contributor.funder Ministerio de Ciencia e Innovación
dc.contributor.funder Junta de Comunidades de Castilla La Mancha


This item appears in the following Collection(s)

Show simple item record