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    Por favor, use este identificador para citar o enlazar este ítem:https://uvadoc.uva.es/handle/10324/53029

    Título
    Compelling new electrocardiographic markers for automatic diagnosis
    Autor
    Rueda Sabater, María CristinaAutoridad UVA
    Fernández Martínez, ItziarAutoridad UVA Orcid
    Larriba González, YolandaAutoridad UVA Orcid
    Rodríguez Collado, AlejandroAutoridad UVA Orcid
    Canedo Ortega, ChristianAutoridad UVA Orcid
    Año del Documento
    2022
    Editorial
    Elsevier
    Descripción
    Producción Científica
    Documento Fuente
    Computer Methods and Programs in Biomedicine, 2022, vol. 221, 106807
    Resumen
    Background and Objective: The automatic diagnosis of heart diseases from the electrocardiogram (ECG) signal is crucial in clinical decision-making. However, the use of computer-based decision rules in clinical practice is still deficient, mainly due to their complexity and a lack of medical interpretation. The objetive of this research is to address these issues by providing valuable diagnostic rules that can be easily implemented in clinical practice. In this research, efficient diagnostic rules friendly in clinical practice are provided. Methods: In this paper, interesting parameters obtained from the ECG signals analysis are presented and two simple rules for automatic diagnosis of Bundle Branch Blocks are defined using new markers derived from the so-called FMM delineator. The main advantages of these markers are the good statistical properties and their clear interpretation in clinically meaningful terms. Results: High sensitivity and specificity values have been obtained using the proposed rules with data from more than 35000 patients from well known benchmarking databases. In particular, to identify Complete Left Bundle Branch Blocks and differentiate this condition from subjects without heart diseases, sensitivity and specificity values ranging from 93% to 99% and from 96% to 99%, respectively. The new markers and the automatic diagnosis are easily available at https://fmmmodel.shinyapps.io/fmmEcg/, an app specifically developed for any given ECG signal. Conclusions: The proposal is different from others in the literature and it is compelling for three main reasons. On the one hand, the markers have a concise electrocardiographic interpretation. On the other hand, the diagnosis rules have a very high accuracy. Finally, the markers can be provided by any device that registers the ECG signal and the automatic diagnosis is made straightforwardly, in contrast to the black-box and deep learning algorithms.
    Palabras Clave
    Electrocardiographic markers
    Marcadores electrocardiográficos
    ISSN
    0169-2607
    Revisión por pares
    SI
    DOI
    10.1016/j.cmpb.2022.106807
    Patrocinador
    Ministerio de Ciencia, Innovación y Universidades (grant PID2019-106363RB-I00)
    Version del Editor
    https://www.sciencedirect.com/science/article/pii/S0169260722001894?via%3Dihub
    Propietario de los Derechos
    © 2022 Elsevier
    Idioma
    eng
    URI
    https://uvadoc.uva.es/handle/10324/53029
    Tipo de versión
    info:eu-repo/semantics/publishedVersion
    Derechos
    openAccess
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