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A Deep Learning Approach for Featureless Robust Quality Assessment of Intermittent Atrial Fibrillation Recordings from Portable and Wearable Devices

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A Deep Learning Approach for Featureless Robust Quality Assessment of Intermittent Atrial Fibrillation Recordings from Portable and Wearable Devices

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dc.contributor.author Huerta Herraiz, Álvaro es_ES
dc.contributor.author Martínez-Rodrigo, Arturo es_ES
dc.contributor.author Bertomeu-González, Vicente es_ES
dc.contributor.author Quesada, Aurelio es_ES
dc.contributor.author Rieta, J J es_ES
dc.contributor.author Alcaraz, Raúl es_ES
dc.date.accessioned 2021-06-12T03:34:20Z
dc.date.available 2021-06-12T03:34:20Z
dc.date.issued 2020-07 es_ES
dc.identifier.issn 1099-4300 es_ES
dc.identifier.uri http://hdl.handle.net/10251/167881
dc.description.abstract [EN] Atrial fibrillation (AF) is the most common heart rhythm disturbance in clinical practice. It often starts with asymptomatic and very short episodes, which are extremely difficult to detect without long-term monitoring of the patient's electrocardiogram (ECG). Although recent portable and wearable devices may become very useful in this context, they often record ECG signals strongly corrupted with noise and artifacts. This impairs automatized ulterior analyses that could only be conducted reliably through a previous stage of automatic identification of high-quality ECG intervals. So far, a variety of techniques for ECG quality assessment have been proposed, but poor performances have been reported on recordings from patients with AF. This work introduces a novel deep learning-based algorithm to robustly identify high-quality ECG segments within the challenging environment of single-lead recordings alternating sinus rhythm, AF episodes and other rhythms. The method is based on the high learning capability of a convolutional neural network, which has been trained with 2-D images obtained when turning ECG signals into wavelet scalograms. For its validation, almost 100,000 ECG segments from three different databases have been analyzed during 500 learning-testing iterations, thus involving more than 320,000 ECGs analyzed in total. The obtained results have revealed a discriminant ability to detect high-quality and discard low-quality ECG excerpts of about 93%, only misclassifying around 5% of clean AF segments as noisy ones. In addition, the method has also been able to deal with raw ECG recordings, without requiring signal preprocessing or feature extraction as previous stages. Consequently, it is particularly suitable for portable and wearable devices embedding, facilitating early detection of AF as well as other automatized diagnostic facilities by reliably providing high-quality ECG excerpts to further processing stages. es_ES
dc.description.sponsorship This research has been supported by grants DPI2017-83952-C3 from MINECO/AEI/FEDER EU, SBPLY/17/180501/000411 from Junta de Comunidades de Castilla-La Mancha and AICO/2019/036 from Generalitat Valenciana. es_ES
dc.language Inglés es_ES
dc.publisher MDPI AG es_ES
dc.relation.ispartof Entropy es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Atrial fibrillation es_ES
dc.subject Continuous wavelet transform es_ES
dc.subject Convolutional neural network es_ES
dc.subject Deep learning es_ES
dc.subject Quality assessment es_ES
dc.subject Single-lead ECG es_ES
dc.subject.classification TECNOLOGIA ELECTRONICA es_ES
dc.title A Deep Learning Approach for Featureless Robust Quality Assessment of Intermittent Atrial Fibrillation Recordings from Portable and Wearable Devices es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/e22070733 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/JCCM//SBPLY%2F17%2F180501%2F000411/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/DPI2017-83952-C3-1-R/ES/ESTUDIO MULTICENTRICO PARA LA EVALUACION DEL SUSTRATO ARRITMOGENICO EN PACIENTES CON FIBRILACION AURICULAR. APLICACION A LA ABLACION POR CATETER/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/GVA//AICO%2F2019%2F036/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Electrónica - Departament d'Enginyeria Electrònica es_ES
dc.description.bibliographicCitation Huerta Herraiz, Á.; Martínez-Rodrigo, A.; Bertomeu-González, V.; Quesada, A.; Rieta, JJ.; Alcaraz, R. (2020). A Deep Learning Approach for Featureless Robust Quality Assessment of Intermittent Atrial Fibrillation Recordings from Portable and Wearable Devices. Entropy. 22(7):1-17. https://doi.org/10.3390/e22070733 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.3390/e22070733 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 17 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 22 es_ES
dc.description.issue 7 es_ES
dc.identifier.pmid 33286505 es_ES
dc.identifier.pmcid PMC7517279 es_ES
dc.relation.pasarela S\435169 es_ES
dc.contributor.funder Generalitat Valenciana es_ES
dc.contributor.funder Agencia Estatal de Investigación es_ES
dc.contributor.funder European Regional Development Fund es_ES
dc.contributor.funder Junta de Comunidades de Castilla-La Mancha es_ES
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