{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T04:10:29Z","timestamp":1774671029091,"version":"3.50.1"},"reference-count":69,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2021,4,3]],"date-time":"2021-04-03T00:00:00Z","timestamp":1617408000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Spanish Ministry of Economy and Competitiveness, the European Regional Development Fund","award":["RTI2018-094449-A-I00-AR"],"award-info":[{"award-number":["RTI2018-094449-A-I00-AR"]}]},{"DOI":"10.13039\/501100003359","name":"Generalitat Valenciana","doi-asserted-by":"publisher","award":["AICO\/2019\/220"],"award-info":[{"award-number":["AICO\/2019\/220"]}],"id":[{"id":"10.13039\/501100003359","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Preterm birth is the leading cause of death in newborns and the survivors are prone to health complications. Threatened preterm labor (TPL) is the most common cause of hospitalization in the second half of pregnancy. The current methods used in clinical practice to diagnose preterm labor, the Bishop score or cervical length, have high negative predictive values but not positive ones. In this work we analyzed the performance of computationally efficient classification algorithms, based on electrohysterographic recordings (EHG), such as random forest (RF), extreme learning machine (ELM) and K-nearest neighbors (KNN) for imminent labor (&lt;7 days) prediction in women with TPL, using the 50th or 10th\u201390th percentiles of temporal, spectral and nonlinear EHG parameters with and without obstetric data inputs. Two criteria were assessed for the classifier design: F1-score and sensitivity. RFF1_2 and ELMF1_2 provided the highest F1-score values in the validation dataset, (88.17 \u00b1 8.34% and 90.2 \u00b1 4.43%) with the 50th percentile of EHG and obstetric inputs. ELMF1_2 outperformed RFF1_2 in sensitivity, being similar to those of ELMSens (sensitivity optimization). The 10th\u201390th percentiles did not provide a significant improvement over the 50th percentile. KNN performance was highly sensitive to the input dataset, with a high generalization capability.<\/jats:p>","DOI":"10.3390\/s21072496","type":"journal-article","created":{"date-parts":[[2021,4,3]],"date-time":"2021-04-03T22:03:36Z","timestamp":1617487416000},"page":"2496","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Optimization of Imminent Labor Prediction Systems in Women with Threatened Preterm Labor Based on Electrohysterography"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9362-5055","authenticated-orcid":false,"given":"Gema","family":"Prats-Boluda","sequence":"first","affiliation":[{"name":"Centro de Investigaci\u00f3n e Innovaci\u00f3n en Bioingenier\u00eda, Universitat Polit\u00e8cnica de Val\u00e8ncia, 46022 Valencia, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7245-6790","authenticated-orcid":false,"given":"Julio","family":"Pastor-Tronch","sequence":"additional","affiliation":[{"name":"Centro de Investigaci\u00f3n e Innovaci\u00f3n en Bioingenier\u00eda, Universitat Polit\u00e8cnica de Val\u00e8ncia, 46022 Valencia, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1410-2721","authenticated-orcid":false,"given":"Javier","family":"Garcia-Casado","sequence":"additional","affiliation":[{"name":"Centro de Investigaci\u00f3n e Innovaci\u00f3n en Bioingenier\u00eda, Universitat Polit\u00e8cnica de Val\u00e8ncia, 46022 Valencia, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rogelio","family":"Monfort-Ort\u00edz","sequence":"additional","affiliation":[{"name":"Servicio de Obstetricia, Hospital Universitario y Polit\u00e9cnico de La Fe, 46026 Valencia, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2221-2560","authenticated-orcid":false,"given":"Alfredo","family":"Perales Mar\u00edn","sequence":"additional","affiliation":[{"name":"Servicio de Obstetricia, Hospital Universitario y Polit\u00e9cnico de La Fe, 46026 Valencia, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vicente","family":"Diago","sequence":"additional","affiliation":[{"name":"Servicio de Obstetricia, Hospital Universitario y Polit\u00e9cnico de La Fe, 46026 Valencia, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alba","family":"Roca Prats","sequence":"additional","affiliation":[{"name":"Servicio de Obstetricia, Hospital Universitario y Polit\u00e9cnico de La Fe, 46026 Valencia, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiyao","family":"Ye-Lin","sequence":"additional","affiliation":[{"name":"Centro de Investigaci\u00f3n e Innovaci\u00f3n en Bioingenier\u00eda, Universitat Polit\u00e8cnica de Val\u00e8ncia, 46022 Valencia, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,3]]},"reference":[{"key":"ref_1","unstructured":"Behrman, R.E., and Butler, A.S. (2007). Preterm Birth: Causes, Consequences, and Prevention. Preterm Birth: Causes, Consequences, and Prevention, National Academies Press."},{"key":"ref_2","unstructured":"Levels and Trends in Child Mortality Report 2019 (2021, April 01). United Nations Children\u2019s Fund; UN Inter-agency group for child mortality estimation.United Nations Children\u2019s. Available online: https:\/\/www.unicef.org\/media\/79371\/file\/UN-IGME-child-mortality-report-2020.pdf.pdf."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"S1","DOI":"10.1186\/1742-4755-10-S1-S1","article-title":"Born too soon: Preterm birth matters","volume":"10","author":"Howson","year":"2013","journal-title":"Reprod. Health"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Godeluck, A., Godeluck, A., G\u00e9rardin, P., Lenclume, V., Mussard, C., Robillard, P.Y., Samp\u00e9riz, S., Benhammou, V., Truffert, P., and Ancel, P.Y. (2019). Mortality and severe morbidity of very preterm infants: Comparison of two French cohort studies. BMC Pediatr., 19.","DOI":"10.1186\/s12887-019-1700-7"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Roberts, D., Brown, J., Medley, N., and Dalziel, S.R. (2017). Antenatal Corticosteroids for Accelerating Fetal Lung Maturation for Women at Risk of Preterm Birth. Cochrane Database of Systematic Reviews, John Wiley and Sons Ltd.","DOI":"10.1002\/14651858.CD004454.pub3"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/j.semcdb.2007.05.004","article-title":"Physiology and electrical activity of uterine contractions","volume":"18","author":"Garfield","year":"2007","journal-title":"Semin. Dev. Biol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1047","DOI":"10.1001\/jama.2017.1373","article-title":"Predictive accuracy of serial transvaginal cervical lengths and quantitative vaginal fetal fibronectin levels for spontaneous preterm birth among nulliparous women","volume":"317","author":"Elovitz","year":"2017","journal-title":"JAMA J. Am. Med. Assoc."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Berghella, V., Hayes, E., Visintine, J., and Baxter, J.K. (2008). Fetal Fibronectin Testing for Reducing the Risk of Preterm Birth. Cochrane Database of Systematic Reviews, John Wiley & Sons, Ltd.","DOI":"10.1002\/14651858.CD006843.pub2"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"217.e1","DOI":"10.1016\/j.ajog.2013.06.046","article-title":"Costs of unnecessary admissions and treatments for \u2018threatened preterm labor\u2019","volume":"209","author":"Lucovnik","year":"2013","journal-title":"Am. J. Obstet. Gynecol."},{"key":"ref_10","first-page":"178","article-title":"Term delivery after hospitalization for preterm labor: Incidence and costs in california","volume":"5","author":"Grover","year":"1998","journal-title":"Prim. Care Update Ob Gyns"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1016\/j.ajog.2008.08.003","article-title":"Can myometrial electrical activity identify patients in preterm labor?","volume":"199","author":"Most","year":"2008","journal-title":"Am. J. Obstet. Gynecol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1007\/s10439-006-9248-8","article-title":"Identification of human term and preterm labor using artificial neural networks on uterine electromyography data","volume":"35","author":"Maner","year":"2007","journal-title":"Ann. Biomed. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1636","DOI":"10.1016\/0002-9378(93)90456-S","article-title":"Uterine electromyography: A critical review","volume":"169","author":"Devedeux","year":"1993","journal-title":"Am. J. Obstet. Gynecol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.biomed.2013.04.007","article-title":"Patterns of electrical activity synchronization in the pregnant rat uterus","volume":"3","author":"Chkeir","year":"2013","journal-title":"BioMedicine"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1016\/j.bspc.2018.07.018","article-title":"Uterine contractile efficiency indexes for labor prediction: A bivariate approach from multichannel electrohysterographic records","volume":"46","author":"Perales","year":"2018","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1097\/OGX.0b013e3181a8c6b1","article-title":"Accuracy of frequency-related parameters of the electrohysterogram for predicting preterm delivery: A review of the literature","volume":"64","author":"Vinken","year":"2009","journal-title":"Obs. Gynecol. Surv."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1016\/j.bbe.2016.06.004","article-title":"Early predicting a risk of preterm labour by analysis of antepartum electrohysterographic signals","volume":"36","author":"Horoba","year":"2016","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"875","DOI":"10.1109\/TBME.2017.2723933","article-title":"Dedicated Entropy Measures for Early Assessment of Pregnancy Progression From Single-Channel Electrohysterography","volume":"65","author":"Mischi","year":"2018","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"911","DOI":"10.1007\/s11517-008-0350-y","article-title":"A comparison of various linear and non-linear signal processing techniques to separate uterine EMG records of term and pre-term delivery groups","volume":"46","author":"Kavsek","year":"2008","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Mas-Cabo, J., Ye-Lin, Y., Garcia-Casado, J., D\u00edaz-Martinez, A., Perales-Marin, A., Monfort-Ortiz, R., Roca-Prats, A., L\u00f3pez-Corral, \u00c1., Prats-Boluda, G., and Diaz-Martinez, A. (2020). Robust Characterization of the Uterine Myoelectrical Activity in Different Obstetric Scenarios. Entropy, 22.","DOI":"10.3390\/e22070743"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.neucom.2015.01.107","article-title":"Advanced artificial neural network classification for detecting preterm births using EHG records","volume":"188","author":"Fergus","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.compbiomed.2017.04.013","article-title":"Automated detection of premature delivery using empirical mode and wavelet packet decomposition techniques with uterine electromyogram signals","volume":"85","author":"Acharya","year":"2017","journal-title":"Comput. Biol. Med."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.cmpb.2017.10.018","article-title":"Identification of preterm birth based on RQA analysis of electrohysterograms","volume":"153","author":"Borowska","year":"2018","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.compbiomed.2020.103677","article-title":"Accurate diagnosis of term\u2013preterm births by spectral analysis of electrohysterography signals","volume":"119","author":"Degbedzui","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1007\/s11517-018-1888-y","article-title":"Uterine electromyography for discrimination of labor imminence in women with threatened preterm labor under tocolytic treatment","volume":"57","author":"Perales","year":"2019","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.bspc.2019.04.001","article-title":"Characterization of the effects of Atosiban on uterine electromyograms recorded in women with threatened preterm labor","volume":"52","author":"Perales","year":"2019","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Mas-Cabo, J., Prats-Boluda, G., Garcia-Casado, J., Alberola-Rubio, J., Monfort-Ortiz, R., Martinez-Saez, C., Perales, A., and Ye-Lin, Y. (2020). Electrohysterogram for ann-based prediction of imminent labor in women with threatened preterm labor undergoing tocolytic therapy. Sensors, 20.","DOI":"10.3390\/s20092681"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Chen, L., and Hao, Y. (2017). Feature Extraction and Classification of EHG between Pregnancy and Labour Group Using Hilbert-Huang Transform and Extreme Learning Machine. Comput. Math. Methods Med., 1\u20139.","DOI":"10.1155\/2017\/7949507"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1016\/j.bbe.2019.12.003","article-title":"Evaluation of electrohysterogram measured from different gestational weeks for recognizing preterm delivery: A preliminary study using random Forest","volume":"40","author":"Peng","year":"2020","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_30","first-page":"1","article-title":"Detection of preterm birth in electrohysterogram signals based on wavelet transform and stacked sparse autoencoder","volume":"14","author":"Chen","year":"2019","journal-title":"PLoS ONE"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1371\/journal.pone.0132116","article-title":"Improved Prediction of Preterm Delivery Using Empirical Mode Decomposition Analysis of Uterine Electromyography Signals","volume":"10","author":"Ren","year":"2015","journal-title":"PLoS ONE"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Mas-Cabo, J., Prats-Boluda, G., Garcia-Casado, J., Alberola Rubio, J., Perales Mar\u00edn, A.J., and 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. J. Sens., 1\u201313.","DOI":"10.1155\/2019\/5373810"},{"key":"ref_33","first-page":"1872","article-title":"Spectral characterization of human EHG frequency components based on the extraction and reconstruction of the ridges in the scalogram","volume":"2007","author":"Terrien","year":"2007","journal-title":"Conf. Proc. IEEE Eng. Med. Biol. Soc."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1169","DOI":"10.1007\/s11760-014-0655-2","article-title":"Selection algorithm for parameters to characterize uterine EHG signals for the detection of preterm labor","volume":"8","author":"Alamedine","year":"2014","journal-title":"Signal Image Video Process."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1016\/j.bbe.2015.11.005","article-title":"Early diagnosis of threatened premature labor by electrohysterographic recordings\u2014The use of digital signal processing","volume":"36","author":"Lemancewicz","year":"2016","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_36","first-page":"144","article-title":"Evaluating Uterine Electrohysterogram with Entropy","volume":"Volume 16","author":"Vrhovec","year":"2007","journal-title":"11th Mediterranean Conference on Medical and Biomedical Engineering and Computing"},{"key":"ref_37","first-page":"1","article-title":"A multi variate multiscale fuzzy entropy algorithm with application to uterine EMG complexity analysis","volume":"19","author":"Ahmed","year":"2017","journal-title":"Entropy"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1424","DOI":"10.1109\/10.966601","article-title":"EEG complexity as a measure of depth of anesthesia for patients","volume":"48","author":"Zhang","year":"2001","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_39","unstructured":"Moslem, B., Hassan, M., Khalil, M., Marque, C., and Diab, M.O. (2009). Monitoring the progress of pregnancy and detecting labor using uterine electromyography. Proceedings of the 2009 International Symposium On Bioelectronics; Bioinformatics, RMIT University."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"761","DOI":"10.1016\/j.medengphy.2014.01.009","article-title":"Performance analysis of four nonlinearity analysis methods using a model with variable complexity and application to uterine EMG signals","volume":"36","author":"Diab","year":"2014","journal-title":"Med. Eng. Phys."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1475-925X-8-17","article-title":"Complex correlation measure: A novel descriptor for Poincar\u00e9 plot","volume":"8","author":"Karmakar","year":"2009","journal-title":"Biomed. Eng. Online"},{"key":"ref_42","first-page":"317","article-title":"Nonlinear Methods to Assess Changes in Heart Rate Variability in Type 2 Diabetic Patients","volume":"10","author":"Roy","year":"2013","journal-title":"Arq. Bras. Cardiol."},{"key":"ref_43","first-page":"107","article-title":"New technique based on uterine electromyography nonlinearity for preterm delivery detection New technique based on uterine electromyography nonlinearity for preterm delivery detection","volume":"6","author":"Naeem","year":"2014","journal-title":"J. Eng. Technol. Res."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic Minority Over-Sampling Technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1088\/0967-3334\/36\/2\/341","article-title":"Separating sets of term and pre-term uterine EMG records","volume":"36","author":"Smrdel","year":"2015","journal-title":"Physiol. Meas."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Naeem, S.M., Ali, A.F., and Eldosok Mohamed, M.A. (2013, January 16\u201318). Comparison between Using Linear and Non-linear Features to classify Uterine Electromyography Signals of Term and Preterm Deliveries. Proceedings of the National Radio Science Conference, NRSC, Cairo, Egypt.","DOI":"10.1109\/NRSC.2013.6587953"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"15","DOI":"10.5121\/ijdkp.2013.3402","article-title":"Imbalanced Data Learning Approaches Review","volume":"3","author":"Bekkar","year":"2013","journal-title":"Int. J. Data Min. Knowl. Manag. Process."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v077.i01","article-title":"Ranger: A fast implementation of random forests for high dimensional data in C++ and R","volume":"77","author":"Wright","year":"2017","journal-title":"J. Stat. Softw."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","article-title":"Extreme learning machine: Theory and applications","volume":"70","author":"Huang","year":"2006","journal-title":"Neurocomputing"},{"key":"ref_50","unstructured":"Hechenbichler, K., and Schliep, K. (2004). Weighted k-Nearest-Neighbor Techniques and Ordinal Classification Projektpartner Weighted k-Nearest-Neighbor Techniques and Ordinal Classification, Ludwig-Maximilians-Universit\u00e4t M\u00fcnchen. 2004 Discussion Paper 399, SFB 386."},{"key":"ref_51","first-page":"1","article-title":"Precision-Recall-Gain Curves: PR Analysis Done Right","volume":"28","author":"Flach","year":"2015","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_52","first-page":"1","article-title":"Comparison of different EHG feature selection methods for the detection of preterm labor","volume":"2013","author":"Alamedine","year":"2013","journal-title":"Comput. Med."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Esteves, G., and Mendes-Moreira, J. (2016, January 19\u201321). Churn perdiction in the telecom business. Proceedings of the 11th International Conference on Digital Information Management, ICDIM 2016, Porto, Portugal.","DOI":"10.1109\/ICDIM.2016.7829775"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Kayabasi, A., Yildiz, B., Aslan, M.F., and Durdu, A. (2018, January 28\u201330). Comparison of ELM and ANN on EMG Signals Obtained for Control of Robotic-Hand. Proceedings of the 10th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2018, Iasi, Romania.","DOI":"10.1109\/ECAI.2018.8679074"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Fergus, P., Cheung, P., Hussain, A., Al-Jumeily, D., Dobbins, C., and Iram, S. (2013). Prediction of preterm deliveries from EHG signals using machine learning. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0077154"},{"key":"ref_56","first-page":"2395","article-title":"Preterm Birth Prediction Using EHG Signals","volume":"5","year":"2019","journal-title":"Int. J. Sci. Res. Eng. Trends"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Idowu, I.O., Fergus, P., Hussain, A., Dobbins, C., Khalaf, M., Casana Eslava, R.V., and Keight, R. (2015, January 26\u201328). Artificial Intelligence for Detecting Preterm Uterine Activity in Gynacology and Obstertric Care. Proceedings of the 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing, Liverpool, UK.","DOI":"10.1109\/CIT\/IUCC\/DASC\/PICOM.2015.31"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"897","DOI":"10.1007\/s42835-019-00118-9","article-title":"Multivariate Time\u2013Frequency Analysis of Electrohysterogram for Classification of Term and Preterm Labor","volume":"14","author":"You","year":"2019","journal-title":"J. Electr. Eng. Technol."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"7","DOI":"10.9756\/BIJRCE.8030","article-title":"ANN, SVM and KNN Classifiers for Prognosis of Cardiac Ischemia\u2014A Comparison","volume":"5","author":"Murthy","year":"2015","journal-title":"Bonfring Int. J. Res. Commun. Eng."},{"key":"ref_60","first-page":"143","article-title":"Comparing ANN, LDA, QDA, KNN and SVM algorithms in classifying relaxed and stressful mental state from two-channel prefrontal EEG data","volume":"3","author":"Aditya","year":"2012","journal-title":"Int. J. Artif. Intell. Soft Comput."},{"key":"ref_61","first-page":"316","article-title":"Interplay of cytokines in preterm birth","volume":"146","author":"Pandey","year":"2017","journal-title":"Indian J. Med. Res."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"633","DOI":"10.2147\/IJWH.S89317","article-title":"Prevention of preterm delivery: Current challenges and future prospects","volume":"8","author":"Koullali","year":"2016","journal-title":"Int. J. Womens Health"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"198363","DOI":"10.1155\/2015\/198363","article-title":"A review of feature selection and feature extraction methods applied on microarray data","volume":"2015","author":"Hira","year":"2015","journal-title":"Adv. Bioinform."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-020-00327-4","article-title":"Selecting critical features for data classification based on machine learning methods","volume":"7","author":"Chen","year":"2020","journal-title":"J. Big Data"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"4370","DOI":"10.1016\/j.ygeno.2020.07.027","article-title":"Integration of multi-objective PSO based feature selection and node centrality for medical datasets","volume":"112","author":"Rostami","year":"2020","journal-title":"Genomics"},{"key":"ref_66","first-page":"1","article-title":"Automatic Identification of Motion Artifacts in EHG Recording for Robust Analysis of Uterine Contractions","volume":"2014","author":"Perales","year":"2014","journal-title":"Comput. Math. Methods Med."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1016\/j.irbm.2018.10.008","article-title":"Detection of Movement Artefacts and Contraction Bursts Using Accelerometer and Electrohysterograms for Home Monitoring of Pregnancy","volume":"39","author":"Happillon","year":"2018","journal-title":"IRBM"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.compbiomed.2019.103394","article-title":"Evaluation of convolutional neural network for recognizing uterine contractions with electrohysterogram","volume":"113","author":"Hao","year":"2019","journal-title":"Comput. Biol. Med."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12884-018-1778-1","article-title":"Automated electrohysterographic detection of uterine contractions for monitoring of pregnancy: Feasibility and prospects","volume":"18","author":"Muszynski","year":"2018","journal-title":"BMC Pregnancy Childbirth"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/7\/2496\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:33:44Z","timestamp":1760362424000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/7\/2496"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,3]]},"references-count":69,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2021,4]]}},"alternative-id":["s21072496"],"URL":"https:\/\/doi.org\/10.3390\/s21072496","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,3]]}}}