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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 \u201cseen\u201d by the models, to determine their generalization capacity. Classifiers\u2019 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 <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M1\"><mml:mn>91.1<\/mml:mn><mml:mo>\u00b1<\/mml:mo><mml:mn>2.5<\/mml:mn><mml:mi>%<\/mml:mi><\/mml:math> for the test dataset. These results show promise for the transfer of the EHG technique to preterm labor prediction in clinical practice.<\/jats:p>","DOI":"10.1155\/2019\/5373810","type":"journal-article","created":{"date-parts":[[2019,11,25]],"date-time":"2019-11-25T18:31:11Z","timestamp":1574706671000},"page":"1-13","source":"Crossref","is-referenced-by-count":10,"title":["Design and Assessment of a Robust and Generalizable ANN-Based Classifier for the Prediction of Premature Birth by means of Multichannel Electrohysterographic Records"],"prefix":"10.1155","volume":"2019","author":[{"given":"J.","family":"Mas-Cabo","sequence":"first","affiliation":[{"name":"Centro de Investigaci\u00f3n e Innovaci\u00f3n en Bioingenier\u00eda, Universitat Polit\u00e8cnica de Val\u00e8ncia, Camino de Vera s\/n Ed.8B, 46022 Valencia, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9362-5055","authenticated-orcid":true,"given":"G.","family":"Prats-Boluda","sequence":"additional","affiliation":[{"name":"Centro de Investigaci\u00f3n e Innovaci\u00f3n en Bioingenier\u00eda, Universitat Polit\u00e8cnica de Val\u00e8ncia, Camino de Vera s\/n Ed.8B, 46022 Valencia, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1410-2721","authenticated-orcid":true,"given":"J.","family":"Garcia-Casado","sequence":"additional","affiliation":[{"name":"Centro de Investigaci\u00f3n e Innovaci\u00f3n en Bioingenier\u00eda, Universitat Polit\u00e8cnica de Val\u00e8ncia, Camino de Vera s\/n Ed.8B, 46022 Valencia, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"J.","family":"Alberola-Rubio","sequence":"additional","affiliation":[{"name":"Servicio de Obstetricia y Ginecolog\u00eda, Hospital Universitario y Polit\u00edcnico La Fe de Valencia, Av. 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