{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T16:38:31Z","timestamp":1793378311064,"version":"4.5.0"},"reference-count":63,"publisher":"Public Library of Science (PLoS)","issue":"8","license":[{"start":{"date-parts":[[2018,8,28]],"date-time":"2018-08-28T00:00:00Z","timestamp":1535414400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Slovenian Research Agency (ARRS) (https:\/\/www.arrs.gov.si\/)","award":["P3-0124 - Metabolic and inborn factors of reproductive health, birth II"],"award-info":[{"award-number":["P3-0124 - Metabolic and inborn factors of reproductive health, birth II"]}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"DOI":"10.1371\/journal.pone.0202125","type":"journal-article","created":{"date-parts":[[2018,8,28]],"date-time":"2018-08-28T18:45:33Z","timestamp":1535481933000},"page":"e0202125","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":71,"title":["Characterization and automatic classification of preterm and term uterine records"],"prefix":"10.1371","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5442-0209","authenticated-orcid":true,"given":"Franc","family":"Jager","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sonja","family":"Liben\u0161ek","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ksenija","family":"Ger\u0161ak","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2018,8,28]]},"reference":[{"key":"ref1","unstructured":"WHO. Born Too Soon: The Global Action Report on Preterm Birth. 2012."},{"key":"ref2","first-page":"436","article-title":"Obstetrics by Ten Teachers","author":"PN Baker","year":"2011"},{"key":"ref3","first-page":"402","article-title":"Prediction and early detection of preterm labor","volume":"101","author":"JD Iams","year":"2003","journal-title":"Am Col Obstet Gynecol"},{"issue":"12","key":"ref4","doi-asserted-by":"crossref","first-page":"1182","DOI":"10.1109\/TBME.1986.325698","article-title":"Uterine EHG processing for obstetrical monitoring","volume":"333","author":"C Marque","year":"1986","journal-title":"IEEE Trans Biomed Eng"},{"key":"ref5","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/S0029-7844(97)83837-9","article-title":"Electrical activity of human uterus during pregnancy as recorded from the abdominal surface","volume":"90","author":"C Buhimschi","year":"1997","journal-title":"Obstet Gynecol"},{"issue":"2","key":"ref6","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/S0301-2115(00)00418-8","article-title":"Uterine electrical activity as predictor of preterm birth in women with preterm contractions","volume":"95","author":"I Verdenik","year":"2001","journal-title":"Eur J Obstet Gynecol Reprod Biol"},{"issue":"6","key":"ref7","first-page":"1254","article-title":"Predicting term and preterm delivery with transabdominal uterine electromyography","volume":"101","author":"WL Maner","year":"2003","journal-title":"Obstet Gynecol"},{"key":"ref8","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1186\/1471-2393-14-192","article-title":"Study protocol: PoPE-Prediction of preterm delivery by electrohysterography","volume":"14","author":"H de Lau","year":"2014","journal-title":"BMC Pregnancy and Childbirth"},{"issue":"2","key":"ref9","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1111\/apha.12424","article-title":"Propagation of electrical activity in uterine muscle during pregnancy: A review","volume":"213","author":"C Rabotti","year":"2015","journal-title":"Acta Physiol"},{"issue":"3","key":"ref10","doi-asserted-by":"crossref","first-page":"228.e1","DOI":"10.1016\/j.ajog.2010.09.024","article-title":"Noninvasive uterine electromyography for prediction of preterm delivery","volume":"204","author":"M Lu\u010dovnik","year":"2011","journal-title":"Am J Obstet Gynecol"},{"issue":"1","key":"ref11","doi-asserted-by":"crossref","first-page":"S5","DOI":"10.1186\/1471-2393-7-S1-S5","article-title":"Preterm labour detection by use of a biophysical marker: the uterine electrical activity","volume":"7","author":"CK Marque","year":"2007","journal-title":"BMC Pregnancy Childbirth"},{"issue":"10","key":"ref12","doi-asserted-by":"crossref","first-page":"1222","DOI":"10.1109\/10.790499","article-title":"Use of electrohysterogram signal for characterization of contractions during pregnancy","volume":"46","author":"H Leman","year":"1999","journal-title":"IEEE Transactions on Biomedical Engineering"},{"issue":"5","key":"ref13","doi-asserted-by":"crossref","first-page":"753","DOI":"10.1088\/0967-3334\/26\/5\/014","article-title":"Quantitative analysis of contraction patterns in electrical activity signal of pregnant uterus as an alternative to mechanical approach","volume":"26","author":"J Jezewski","year":"2005","journal-title":"Physiol Meas"},{"key":"ref14","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1016\/j.bbe.2016.08.005","article-title":"Automated detection of uterine contractions in tocography signals\u2014Comparison of algorithms","volume":"36","author":"K Horoba","year":"2016","journal-title":"Biocybernetics and Biomedical Engineering"},{"key":"ref15","doi-asserted-by":"crossref","unstructured":"Jyothi R, Soans RS, Bhat PV. Automatic Classification of Toco-Signals: An Approach Towards Comprehensive Monitoring of Labour Progress. In: Proceedings 2016 International Conference on Signal Processing and Communications (SPCOM); 2016:1-5.","DOI":"10.1109\/SPCOM.2016.7746694"},{"issue":"2","key":"ref16","first-page":"97","article-title":"Recognition of premature threatening labour symptoms from bioelectrical uterine activity signals","volume":"17","author":"J Sikora","year":"2011","journal-title":"Archives of Perinatal Medicine"},{"issue":"2","key":"ref17","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1111\/j.1600-0412.2010.01031.x","article-title":"Use of uterine electromyography to diagnose term and preterm labor","volume":"90","author":"M Lu\u010dovnik","year":"2011","journal-title":"Acta Obstet Gynecol Scand"},{"issue":"3","key":"ref18","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":"WL Maner","year":"2007","journal-title":"Ann Biomed Eng"},{"issue":"4","key":"ref19","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 electrohysterograhic signals","volume":"36","author":"K Horoba","year":"2016","journal-title":"Biocybernetics and Biomedical Engineering"},{"key":"ref20","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1155\/2013\/485684","article-title":"Comparison of Different EHG Feature Selection Methods for the Detection of Preterm Labor","volume":"2013","author":"D Alamedine","year":"2013","journal-title":"Computational and Mathematical Methods in Medicine"},{"issue":"6","key":"ref21","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":"A Diab","year":"2014","journal-title":"Med Eng Phys"},{"issue":"8","key":"ref22","doi-asserted-by":"crossref","first-page":"980","DOI":"10.1016\/j.medengphy.2011.03.010","article-title":"Comparison between approximate entropy, correntropy and time reversibility: application to uterine electromyogram signals","volume":"33","author":"M Hassan","year":"2011","journal-title":"Med Eng Phys"},{"issue":"1","key":"ref23","first-page":"8","article-title":"Classification for uterine EMG signals: comparison between AR model and statistical classification method","volume":"5","author":"MO Diab","year":"2007","journal-title":"Int Journal of Computational Cognition"},{"issue":"167","key":"ref24","first-page":"9","article-title":"Combining data fusion with multiresolution analysis for improving the classification accuracy of uterine EMG signals","volume":"2012","author":"B Moslem","year":"2012","journal-title":"EURASIP Journal on Advances in Signal Processing"},{"issue":"4","key":"ref25","doi-asserted-by":"crossref","first-page":"1160","DOI":"10.1109\/TBME.2012.2229279","article-title":"Better pregnancy monitoring using nonlinear correlation analysis of external uterine electromyography","volume":"60","author":"M Hassan","year":"2013","journal-title":"IEEE Trans Biomed Eng"},{"key":"ref26","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1155\/2017\/7949507","article-title":"Feature Extraction and Classification of EHG between Pregnancy and Labour Group Using Hilbert-Huang Transform and Extreme Learning Machine","volume":"2017","author":"L Chen","year":"2017","journal-title":"Computational and Mathematical Methods in Medicine"},{"key":"ref27","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":"G Fele-\u017dor\u017e","year":"2008","journal-title":"Med Biol Eng Comput"},{"key":"ref28","doi-asserted-by":"crossref","first-page":"e215","DOI":"10.1161\/01.CIR.101.23.e215","article-title":"PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals","volume":"101","author":"AL Goldberger","year":"2000","journal-title":"Circulation"},{"issue":"10","key":"ref29","doi-asserted-by":"crossref","first-page":"e77154","DOI":"10.1371\/journal.pone.0077154","article-title":"Prediction of Preterm Deliveries from EHG Signals Using Machine Learning","volume":"8","author":"P Fergus","year":"2013","journal-title":"PLoS ONE"},{"key":"ref30","doi-asserted-by":"crossref","first-page":"963","DOI":"10.1016\/j.neucom.2014.03.087","article-title":"Dynamic neural network architecture inspired by the immune algorithm to predict preterm deliveries in pregnant women","volume":"151","author":"AJ Hussain","year":"2015","journal-title":"Neurocomputing"},{"key":"ref31","doi-asserted-by":"crossref","unstructured":"Idowu IO, Fergus P, Hussain A, Dobbins C, Khalaf M, Casana Eslava RV, et al. Artificial Intelligence for Detecting Preterm Uterine Activity in Gynecology and Obstetric Care. In: Proceedings 2015 IEEE International Conference on Computer and Information Technology; 2015:215-220.","DOI":"10.1109\/CIT\/IUCC\/DASC\/PICOM.2015.31"},{"key":"ref32","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":"P Fergus","year":"2016","journal-title":"Neurocomputing"},{"issue":"2","key":"ref33","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":"A Smrdel","year":"2015","journal-title":"Physiol Meas"},{"issue":"7","key":"ref34","doi-asserted-by":"crossref","first-page":"e0132116","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":"P Ren","year":"2015","journal-title":"PLoS ONE"},{"key":"ref35","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":"UR Acharya","year":"2017","journal-title":"Comput Biol Med"},{"issue":"1","key":"ref36","doi-asserted-by":"crossref","first-page":"2","DOI":"10.3390\/e19010002","article-title":"A Multivariate Multiscale Fuzzy Entropy Algorithm with Application to Uterine EMG Complexity Analysis","volume":"19","author":"MU Ahmed","year":"2017","journal-title":"Entropy"},{"key":"ref37","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.compbiomed.2016.07.003","article-title":"A multichannel time\u2014frequency and multi-wavelet toolbox for uterine electromyography processing and visualisation","volume":"76","author":"AG Batista","year":"2016","journal-title":"Computers in Biology and Medicine"},{"issue":"6","key":"ref38","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":"D Devedeux","year":"1993","journal-title":"Am J Obstet Gynecol"},{"issue":"2","key":"ref39","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/j.compbiomed.2009.11.019","article-title":"Uterine electromyogram database and processing function interface: An open standard analysis platform for electrohysterogram signals","volume":"40","author":"J Terrien","year":"2010","journal-title":"Computers in Biology and Medicine"},{"issue":"6","key":"ref40","doi-asserted-by":"crossref","first-page":"361","DOI":"10.5405\/jmbe.768","article-title":"Energy Distribution Analysis of Uterine Electromyography Signals","volume":"30","author":"B Moslem","year":"2010","journal-title":"Journal of Medical and Biological Engineering"},{"issue":"6","key":"ref41","doi-asserted-by":"crossref","first-page":"H2039","DOI":"10.1152\/ajpheart.2000.278.6.H2039","article-title":"Physiological time-series analysis using approximate entropy and sample entropy","volume":"278","author":"JS Richman","year":"2000","journal-title":"American Journal of Physiology Heart and Circulatory Physiology"},{"issue":"6","key":"ref42","doi-asserted-by":"crossref","first-page":"068102","DOI":"10.1103\/PhysRevLett.89.068102","article-title":"Multiscale Entropy Analysis of Complex Physiologic Time Series","volume":"89","author":"M Costa","year":"2002","journal-title":"Physical Review Letters"},{"issue":"1","key":"ref43","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1088\/0967-3334\/35\/1\/45","article-title":"A novel technique for muscle onset detection using surface EMG signals without removal of ECG artifacts","volume":"35","author":"P Zhou","year":"2014","journal-title":"Physiol Meas"},{"key":"ref44","unstructured":"Liben\u0161ek S. Characterization and automatic classification of electrical activities of uterus. [MSc thesis], Ljubljana, Slovenia: Faculty of Computer and Information Science, University of Ljubljana; 2014."},{"key":"ref45","article-title":"Elementary Statistics: A Step by Step Approach","author":"AG Bluman","year":"2009"},{"issue":"1","key":"ref46","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1109\/TCOM.1967.1089532","article-title":"The divergence and Bhattacharyya distance measures in signal selection","volume":"15","author":"T Kailath","year":"1967","journal-title":"IEEE Transactions on Communication Technology"},{"issue":"8","key":"ref47","doi-asserted-by":"crossref","first-page":"1703","DOI":"10.1016\/S0031-3203(03)00035-9","article-title":"Feature extraction based on the Bhattacharyya distance","volume":"36","author":"E Choi","year":"2003","journal-title":"Pattern Recognition"},{"issue":"1","key":"ref48","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1214\/aoms\/1177729694","article-title":"On information and sufficiency","volume":"22","author":"S Kullback","year":"1951","journal-title":"Ann Math Stat"},{"issue":"9","key":"ref49","doi-asserted-by":"crossref","first-page":"1100","DOI":"10.1109\/T-C.1971.223410","article-title":"A direct method of nonparametric measurement selection","volume":"C-20","author":"AW Whitney","year":"1971","journal-title":"IEEE Transactions on Computers"},{"key":"ref50","first-page":"1","article-title":"Solutions to Instability Problems with Sequential Wrapper-Based Approaches To Feature Selection","author":"K Dunne","year":"2002","journal-title":"Journal of Machine Learning Research"},{"issue":"8","key":"ref51","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","article-title":"An Introduction to ROC analysis","volume":"27","author":"T Fawcet","year":"2006","journal-title":"Pattern Recognition Letters"},{"issue":"1","key":"ref52","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic Minority Over-Sampling Technique","volume":"16","author":"NV Chawla","year":"2002","journal-title":"Journal of Artificial Intelligence Research"},{"key":"ref53","doi-asserted-by":"crossref","unstructured":"He H, Bai Y, Garcia EA, Li S. ADASYN: Adaptive synthetic sampling approach for imbalanced learning. In: Proceedings 2008 International Joint Conference on Neural Networks (IJCNN); 2008:1322-1328.","DOI":"10.1109\/IJCNN.2008.4633969"},{"issue":"71","key":"ref54","first-page":"11","article-title":"Diagnosing onset of labor: a systematic review of definitions in the research literature","volume":"16","author":"GE Hanley","year":"2016","journal-title":"BMC Pregnancy and Childbirth"},{"issue":"1","key":"ref55","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1111\/j.1471-0528.2005.00596.x","article-title":"Use of uterine EMG and cervical LIF in monitoring pregnant patients","volume":"112","author":"RE Garfield","year":"2005","journal-title":"An International Journal of Obstetrics and Gynaecology"},{"key":"ref56","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":"M Borowska","year":"2018","journal-title":"Computer Methods and Programs in Biomedicine"},{"key":"ref57","doi-asserted-by":"crossref","first-page":"150017","DOI":"10.1038\/sdata.2015.17","article-title":"The Icelandic 16-electrode electrohysterogram database","volume":"2","author":"A Alexandersson","year":"2015","journal-title":"Sci Data"},{"key":"ref58","unstructured":"Diab A, Hassan M, Laforet J, Karlsson B, Marque C. EHG Source Localization Using Signals from a Uterus Electrophysiological Model. In: Proceedings of Virtual Physiological Human Conference; 2014."},{"issue":"2","key":"ref59","first-page":"266","article-title":"Pelvic Scoring for Elective Induction","volume":"24","author":"EH Bishop","year":"1964","journal-title":"Obstetrics & Gynecology"},{"key":"ref60","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/0028-2243(94)90292-5","article-title":"Changes in cervical electromyographic activity and their correlation with the cervical response to myometrial activity during labour","volume":"57","author":"KS Ol\u00e1h","year":"1994","journal-title":"European Journal of Obstetrics & Gynecology and Reproductive Biology"},{"key":"ref61","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.compbiomed.2016.08.001","article-title":"An electro-mechanical multiscale model of uterine pregnancy contraction","volume":"77","author":"M Yochum","year":"2016","journal-title":"Computers in Biology and Medicine"},{"issue":"2","key":"ref62","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1177\/1933719112446082","article-title":"The Electrical Activities of the Uterus During Pregnancy","volume":"20","author":"WJ Lammers","year":"2013","journal-title":"Reproductive Sciences"},{"key":"ref63","doi-asserted-by":"crossref","DOI":"10.1007\/978-0-387-77259-2","article-title":"Physics of the Life Sciences","author":"J Newman","year":"2008"}],"container-title":["PLOS ONE"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/dx.plos.org\/10.1371\/journal.pone.0202125","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,6]],"date-time":"2025-07-06T13:04:56Z","timestamp":1751807096000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0202125"}},"subtitle":[],"editor":[{"given":"Olalekan","family":"Uthman","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2018,8,28]]},"references-count":63,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2018,8,28]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0202125","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/349266","asserted-by":"object"}]},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,8,28]]}}}