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dc.contributor.author
Melzi, Pietro
dc.contributor.author
Tolosana, Ruben
dc.contributor.author
Cecconi, Alberto
dc.contributor.author
Sanz Garcia, Ancor
dc.contributor.author
Ortega, Guillermo José
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dc.contributor.author
Jimenez Borreguero, Luis Jesus
dc.contributor.author
Vera Rodriguez, Ruben
dc.date.available
2022-08-08T16:22:45Z
dc.date.issued
2021-11-23
dc.identifier.citation
Melzi, Pietro; Tolosana, Ruben; Cecconi, Alberto; Sanz Garcia, Ancor; Ortega, Guillermo José; et al.; Analyzing artificial intelligence systems for the prediction of atrial fibrillation from sinus-rhythm ECGs including demographics and feature visualization; Nature Publishing Group; Scientific Reports; 11; 1; 23-11-2021; 1-10
dc.identifier.issn
2045-2322
dc.identifier.uri
http://hdl.handle.net/11336/164586
dc.description.abstract
Atrial fibrillation (AF) is an abnormal heart rhythm, asymptomatic in many cases, that causes several health problems and mortality in population. This retrospective study evaluates the ability of different AI-based models to predict future episodes of AF from electrocardiograms (ECGs) recorded during normal sinus rhythm. Patients are divided into two classes according to AF occurrence or sinus rhythm permanence along their several ECGs registry. In the constrained scenario of balancing the age distributions between classes, our best AI model predicts future episodes of AF with area under the curve (AUC) 0.79 (0.72–0.86). Multiple scenarios and age-sex-specific groups of patients are considered, achieving best performance of prediction for males older than 70 years. These results point out the importance of considering different demographic groups in the analysis of AF prediction, showing considerable performance gaps among them. In addition to the demographic analysis, we apply feature visualization techniques to identify the most important portions of the ECG signals in the task of AF prediction, improving this way the interpretability and understanding of the AI models. These results and the simplicity of recording ECGs during check-ups add feasibility to clinical applications of AI-based models.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Nature Publishing Group
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dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by/2.5/ar/
dc.subject
deep learning
dc.subject
ecg
dc.subject.classification
Otras Medicina Clínica
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dc.subject.classification
Medicina Clínica
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dc.subject.classification
CIENCIAS MÉDICAS Y DE LA SALUD
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dc.title
Analyzing artificial intelligence systems for the prediction of atrial fibrillation from sinus-rhythm ECGs including demographics and feature visualization
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2022-08-02T17:20:08Z
dc.identifier.eissn
2045-2322
dc.journal.volume
11
dc.journal.number
1
dc.journal.pagination
1-10
dc.journal.pais
Reino Unido
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dc.journal.ciudad
Londres
dc.description.fil
Fil: Melzi, Pietro. Universidad Autónoma de Madrid; España
dc.description.fil
Fil: Tolosana, Ruben. Universidad Autónoma de Madrid; España
dc.description.fil
Fil: Cecconi, Alberto. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa; España
dc.description.fil
Fil: Sanz Garcia, Ancor. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa; España
dc.description.fil
Fil: Ortega, Guillermo José. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa; España. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.description.fil
Fil: Jimenez Borreguero, Luis Jesus. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa; España
dc.description.fil
Fil: Vera Rodriguez, Ruben. Universidad Autónoma de Madrid; España
dc.journal.title
Scientific Reports
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/https://www.nature.com/articles/s41598-021-02179-1
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1038/s41598-021-02179-1
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