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Application of Deep Learning for Quality Assessment of Atrial Fibrillation ECG Recordings

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Application of Deep Learning for Quality Assessment of Atrial Fibrillation ECG Recordings

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dc.contributor.author Huerta, Alvaro es_ES
dc.contributor.author Martinez-Rodrigo, Arturo es_ES
dc.contributor.author Arias, Miguel A. es_ES
dc.contributor.author Langley, Philip es_ES
dc.contributor.author Rieta, J J es_ES
dc.contributor.author Alcaraz, Raul es_ES
dc.date.accessioned 2021-12-20T08:39:19Z
dc.date.available 2021-12-20T08:39:19Z
dc.date.issued 2020-09-16 es_ES
dc.identifier.issn 2325-887X es_ES
dc.identifier.uri http://hdl.handle.net/10251/178566
dc.description.abstract [EN] In the last years, atrial fibrillation (AF) has become one of the most remarkable health problems in the developed world. This arrhythmia is associated with an increased risk of cardiovascular events, being its early detection an unresolved challenge. To palliate this issue, long-term wearable electrocardiogram (ECG) recording systems are used, because most of AF episodes are asymptomatic and very short in their initial stages. Unfortunately, portable equipments are very susceptible to be contaminated with different kind of noises, since they work in highly dynamics and ever-changing environments. Within this scenario, the correct identification of free-noise ECG segments results critical for an accurate and robust AF detection. Hence, this work presents a deep learning-based algorithm to identify high-quality intervals in single-lead ECG recordings obtained from patients with paroxysmal AF. The obtained results have provided a remarkable ability to classify between high- and low-quality ECG segments about 92%, only misclassifying around 7% of clean AF intervals as noisy segments. These outcomes have overcome most previous ECG quality assessment algorithms also dealing with AF signals by more than 20%. es_ES
dc.description.sponsorship This research has been supported by the grants DPI2017-83952-C3 from MINECO/AEI/FEDER EU, SBPLY/17/180501/000411 from Junta de Comunidades de Castilla-La Mancha, AICO/2019/036 from Generalitat Valenciana and FEDER 2018/11744. es_ES
dc.language Inglés es_ES
dc.publisher IEEE es_ES
dc.relation.ispartof Computing in Cardiology 2020; Vol 47 es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject.classification TECNOLOGIA ELECTRONICA es_ES
dc.title Application of Deep Learning for Quality Assessment of Atrial Fibrillation ECG Recordings es_ES
dc.type Comunicación en congreso es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.22489/CinC.2020.367 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/JCCM//SBPLY%2F17%2F180501%2F000411//Caracterización del sustrato auricular mediante análisis de señal como herramienta de asistencia procedimental en ablación por catéter de fibrilación auricular/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement///AICO%2F2019%2F036//METODOS DE DIAGNOSTICO Y TERAPIA PERSONALIZADA EN ABLACION POR CATETER DE ARRITMIAS CARDIACAS/ 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, A.; Martinez-Rodrigo, A.; Arias, MA.; Langley, P.; Rieta, JJ.; Alcaraz, R. (2020). Application of Deep Learning for Quality Assessment of Atrial Fibrillation ECG Recordings. IEEE. 1-4. https://doi.org/10.22489/CinC.2020.367 es_ES
dc.description.accrualMethod S es_ES
dc.relation.conferencename 47th Computing in Cardiology Conference (CinC 2020) es_ES
dc.relation.conferencedate Septiembre 13-16,2020 es_ES
dc.relation.conferenceplace Rimini, Italia es_ES
dc.relation.publisherversion https://doi.org/10.22489/CinC.2020.367 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 4 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.relation.pasarela S\433024 es_ES
dc.contributor.funder Junta de Comunidades de Castilla-La Mancha es_ES


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