Mostrar el registro sencillo del ítem
dc.contributor.author | Huerta, Alvaro | es_ES |
dc.contributor.author | Martínez-Rodrigo, Arturo | es_ES |
dc.contributor.author | Bertomeu-González, Vicente | es_ES |
dc.contributor.author | Ayo-Martin, Oscar | es_ES |
dc.contributor.author | Rieta, J J | es_ES |
dc.contributor.author | Alcaraz, Raúl | es_ES |
dc.date.accessioned | 2024-04-22T18:06:53Z | |
dc.date.available | 2024-04-22T18:06:53Z | |
dc.date.issued | 2024-05 | es_ES |
dc.identifier.issn | 1746-8094 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/203675 | |
dc.description.abstract | [EN] Current wearable electrocardiogram (ECG) recording systems have great potential to revolutionize early diagnosis of paroxysmal atrial fibrillation (AF). They are able to continuously acquire an ECG signal for long weeks and then increase the probability of detecting first brief, intermittent signs of the arrhythmia. However, the recorded signal is often broadly corrupted by noise and artifacts, and accurate assessment of its quality to avoid automated misdiagnosis and false alarms of AF is still an unsolved challenge. In this context, the present work is pioneer in exploring the usefulness of transforming the single-lead ECG signal into two common phase space (PS) representations, such as the Poincare plot and the first order difference graph, for evaluation of its quality. Several machine and deep learning models fed with features and images derived from these PS portraits reported a better performance than well-known previous methods, even when they were trained and validated on two separate databases. Indeed, in binary classification of high- and low-quality ECG excerpts, the generated PS-based algorithms reported a discriminant power greater than 85%, misclassifying less than 20% of high-quality AF episodes and non -normal rhythms as noisy excerpts. Moreover, because both PS reconstructions do not require any mathematical transformation, these algorithms also spent much less time in classifying each ECG excerpt in validation and testing stages than previous methods. As a consequence, ECG transformation to both PS portraits enables novel, simple, effective, and computational low-cost techniques, based both on machine and deep learning classifiers, for ECG quality assessment. | es_ES |
dc.description.sponsorship | This research has received financial support from Daiichi Sankyo SLU and from public grants PID2021-00X128525-IV0, PID2021-12380 4OB-I00, and TED2021-130935B-I00 of the Spanish Government 10.13039/501100011033 jointly with the European Regional Development Fund (EU) , SBPLY/21/180501/000186 from Junta de Comunidades de Castilla-La Mancha, Spain, and AICO/2021/286 from Generalitat Valenciana. | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation.ispartof | Biomedical Signal Processing and Control | es_ES |
dc.rights | Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) | es_ES |
dc.subject | Signal quality assessment | es_ES |
dc.subject | Paroxysmal atrial fibrillation | es_ES |
dc.subject | Phase space portraits | es_ES |
dc.subject | Machine learning classifiers | es_ES |
dc.subject | Deep learning algorithms | es_ES |
dc.subject.classification | TECNOLOGIA ELECTRONICA | es_ES |
dc.title | Single-lead electrocardiogram quality assessment in the context of paroxysmal atrial fibrillation through phase space plots | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.1016/j.bspc.2023.105920 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-123804OB-I00/ES/INTELIGENCIA ARTIFICIAL PARA LA MEDICINA MOVIL INNOVADORA EN ENFERMEDADES CARDIOVASCULARES/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-128525OB-I00/ES/DETECCION PRECOZ DE ARRITMIAS CARDIACAS MEDIANTE INTELIGENCIA ARTIFICIAL PARA MEJORAR LA PREVENCION SECUNDARIA DEL ICTUS CRIPTOGENICO/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/GVA//AICO%2F2021%2F286/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/JCCM//SBPLY%2F21%2F180501%2F000186/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/MICINN//TED2021-130935B-I00/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Escuela Politécnica Superior de Gandia - Escola Politècnica Superior de Gandia | es_ES |
dc.description.bibliographicCitation | Huerta, A.; Martínez-Rodrigo, A.; Bertomeu-González, V.; Ayo-Martin, O.; Rieta, JJ.; Alcaraz, R. (2024). Single-lead electrocardiogram quality assessment in the context of paroxysmal atrial fibrillation through phase space plots. Biomedical Signal Processing and Control. 91. https://doi.org/10.1016/j.bspc.2023.105920 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.1016/j.bspc.2023.105920 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 91 | es_ES |
dc.relation.pasarela | S\513714 | 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 | Ministerio de Ciencia e Innovación | es_ES |
dc.contributor.funder | Junta de Comunidades de Castilla-La Mancha | es_ES |