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Design and Assessment of a Robust and Generalizable ANN-Based Classifier for the Prediction of Premature Birth by means of Multichannel Electrohysterographic Records

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Design and Assessment of a Robust and Generalizable ANN-Based Classifier for the Prediction of Premature Birth by means of Multichannel Electrohysterographic Records

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dc.contributor.author Mas-Cabo, Javier es_ES
dc.contributor.author Prats-Boluda, Gema es_ES
dc.contributor.author Garcia-Casado, Javier es_ES
dc.contributor.author Alberola Rubio, José es_ES
dc.contributor.author Perales Marín, Alfredo José es_ES
dc.contributor.author Ye Lin, Yiyao es_ES
dc.date.accessioned 2020-01-22T21:02:19Z
dc.date.available 2020-01-22T21:02:19Z
dc.date.issued 2019 es_ES
dc.identifier.issn 1687-725X es_ES
dc.identifier.uri http://hdl.handle.net/10251/135405
dc.description.abstract [EN] Preterm labor is one of the major causes of neonatal deaths and also the cause of significant health and development impairments in those who survive. However, there are still no reliable and accurate tools for preterm labor prediction in clinical settings. Electrohysterography (EHG) has been proven to provide relevant information on the labor time horizon. Many studies focused on predicting preterm labor by using temporal, spectral, and nonlinear parameters extracted from single EHG recordings. However, multichannel analysis, which includes information from the whole uterus and about coupling between the recording areas, may provide better results. The cross validation method is often used to design classifiers and evaluate their performance. However, when the validation dataset is used to tune the classifier hyperparameters, the performance metrics of this dataset may not properly assess its generalization capacity. In this work, we developed and compared different classifiers, based on artificial neural networks, for predicting preterm labor using EHG features from single and multichannel recordings. A set of temporal, spectral, nonlinear, and synchronization parameters computed from EHG recordings was used as the input features. All the classifiers were evaluated on independent test datasets, which were never ¿seen¿ by the models, to determine their generalization capacity. Classifiers¿ performance was also evaluated when obstetrical data were included. The experimental results show that the classifier performance metrics were significantly lower in the test dataset (AUC range 76-91%) than in the train and validation sets (AUC range 90-99%). The multichannel classifiers outperformed the single-channel classifiers, especially when information was combined into mean efficiency indexes and included coupling information between channels. Including obstetrical data slightly improved the classifier metrics and reached an AUC of for the test dataset. These results show promise for the transfer of the EHG technique to preterm labor prediction in clinical practice. es_ES
dc.description.sponsorship This work was supported by the Spanish Ministry of Economy and Competitiveness, the European Regional Development Fund (DPI2015-68397-R, MINECO/FEDER, and RTI2018-094449-A-I00-AR); Generalitat Valenciana (AICO/2019/220); and the VLC/Campus (UPV-FE-2018-B03). es_ES
dc.language Inglés es_ES
dc.publisher Hindawi Limited es_ES
dc.relation UNIVERSIDAD POLITECNICA DE VALENCIA/UPV-FE-2018-B03 es_ES
dc.relation GENERALITAT VALENCIANA/AICO/2019/220 es_ES
dc.relation MINISTERIO DE ECONOMIA Y EMPRESA/DPI2015-68397-R es_ES
dc.relation AEI/RTI2018-094449-A-I00-AR es_ES
dc.relation.ispartof Journal of Sensors es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject.classification TECNOLOGIA ELECTRONICA es_ES
dc.title Design and Assessment of a Robust and Generalizable ANN-Based Classifier for the Prediction of Premature Birth by means of Multichannel Electrohysterographic Records es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1155/2019/5373810 es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Instituto Interuniversitario de Investigación en Bioingeniería y Tecnología Orientada al Ser Humano - Institut Interuniversitari d'Investigació en Bioenginyeria i Tecnologia Orientada a l'Ésser Humà es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Electrónica - Departament d'Enginyeria Electrònica es_ES
dc.contributor.affiliation Universitat Politècnica de València. Servicio de Alumnado - Servei d'Alumnat es_ES
dc.description.bibliographicCitation Mas-Cabo, J.; Prats-Boluda, G.; Garcia-Casado, J.; Alberola Rubio, J.; Perales Marín, AJ.; Ye Lin, Y. (2019). Design and Assessment of a Robust and Generalizable ANN-Based Classifier for the Prediction of Premature Birth by means of Multichannel Electrohysterographic Records. Journal of Sensors. 2019:1-13. https://doi.org/10.1155/2019/5373810 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1155/2019/5373810 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 13 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 2019 es_ES
dc.relation.pasarela S\398428 es_ES
dc.contributor.funder Generalitat Valenciana es_ES
dc.contributor.funder Agencia Estatal de Investigación es_ES
dc.contributor.funder Ministerio de Economía y Empresa es_ES
dc.contributor.funder Universitat Politècnica de València es_ES


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