dc.creator | Monedero Goicoechea, Iñigo Luis | es |
dc.date.accessioned | 2023-01-19T07:03:24Z | |
dc.date.available | 2023-01-19T07:03:24Z | |
dc.date.issued | 2022-01 | |
dc.identifier.citation | Monedero Goicoechea, I.L. (2022). A novel ECG diagnostic system for the detection of 13 different diseases. Engineering Applications of Artificial Intelligence, 107 (January), 104536. https://doi.org/10.1016/j.engappai.2021.104536. | |
dc.identifier.issn | 0952-1976 | es |
dc.identifier.uri | https://hdl.handle.net/11441/141536 | |
dc.description.abstract | Manual analysis of electrocardiogram (ECG) signals is a laborious and prone-to-error task, even for a specialist
with many hours of experience. For this reason, research on automatic ECG diagnosis is widespread in the
literature and continues to grow each year. The present paper describes a novel and fully functional expert
system for automatic diagnosis of 13 different diseases using standard 12-lead ECGs. This system makes three
significant contributions to the state of the art: (a) the large number of different diseases diagnosed; (b) the
use of 5 leads for a more precise identification and measurement of the ECG waves; and (c) a novel noise
indicator that measures the quality of the acquired ECG signal. The kernel of the system consists of a set of
rules that replicate a specialist’s diagnostic process but with the speed of an automatic system. The rules use a
set of parameters generated after a noise-filtering process of the ECG signal and subsequent identification of its
different waves (P, QRS complex, T, and Delta). The design of the rules was carried out with the collaboration
of a specialist with more than 20 years of experience in ECG diagnosis and using a database of 284,000 ECGs
as support. The system was validated by the specialist, obtaining a reliability of 80.8%. Given the complexity
of the problem and the number of diagnoses covered, the results are considered satisfactory and make the
system a useful support tool for diagnosis. | es |
dc.format | application/pdf | es |
dc.format.extent | 12 p. | es |
dc.language.iso | eng | es |
dc.publisher | Elsevier | es |
dc.relation.ispartof | Engineering Applications of Artificial Intelligence, 107 (January), 104536. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Electrocardiogram | es |
dc.subject | Expert system | es |
dc.subject | Wavelet transform | es |
dc.subject | Decision tree | es |
dc.title | A novel ECG diagnostic system for the detection of 13 different diseases | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Tecnología Electrónica | es |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S0952197621003845 | es |
dc.identifier.doi | 10.1016/j.engappai.2021.104536 | es |
dc.contributor.group | Universidad de Sevilla. TIC150: Tecnología Electrónica e Informática Industrial | es |
dc.journaltitle | Engineering Applications of Artificial Intelligence | es |
dc.publication.volumen | 107 | es |
dc.publication.issue | January | es |
dc.publication.initialPage | 104536 | es |
dc.contributor.funder | Preving Investment Company project ECARDIO | es |