{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T06:08:19Z","timestamp":1788847699225,"version":"build-2803163510"},"reference-count":42,"publisher":"IOP Publishing","issue":"5","license":[{"start":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T00:00:00Z","timestamp":1788825600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/iop-standard\/v1"},{"start":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T00:00:00Z","timestamp":1788825600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Biomed. Phys. Eng. Express"],"published-print":{"date-parts":[[2026,10,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective.<\/jats:italic>\n                    Preterm delivery remains the leading cause of neonatal mortality worldwide. Early detection and accurate prediction of women at risk are critical for improving maternal and neonatal outcomes. This study aimed to diagnose preterm delivery using uterine contraction signals, providing a non-invasive approach to assist in clinical decision-making.\n                    <jats:italic>Method.<\/jats:italic>\n                    Electrohysterography (EHG) and tocodynamometry (TOCO) signals were recorded from a dataset of 300 samples, including both term and preterm deliveries. Linear and nonlinear signal processing techniques were employed to extract a wide range of features across time, frequency, and nonlinear domains. Features derived from return mapping and other methods were utilized as inputs to a multi-layer perceptron (MLP) classifier for distinguishing between term and preterm deliveries.\n                    <jats:italic>Result.<\/jats:italic>\n                    The proposed method achieved exceptional classification performance, with an accuracy of 95.18 \u00b1 1.21%, sensitivity of 93.84 \u00b1 1.76%, and specificity of 95.61 \u00b1 1.09%. These results demonstrate the method\u2019s robustness and reliability. Comparative analysis with recent research highlighted the superior performance of the proposed approach.\n                    <jats:italic>Conclusion.<\/jats:italic>\n                    The findings underscore the potential of a computer-aided diagnostic system for preterm delivery detection using EHG and TOCO signals. This method provides a non-invasive, efficient, and accurate tool that could significantly reduce unnecessary hospitalizations and mitigate risks for high-risk pregnancies. By enabling timely intervention, the proposed approach holds promise for improving maternal and neonatal healthcare outcomes.\n                  <\/jats:p>","DOI":"10.1088\/2057-1976\/ae9ecb","type":"journal-article","created":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T22:48:02Z","timestamp":1787784482000},"page":"055005","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A geometric nonlinear return mapping framework for automated term\u2013preterm delivery classification: a comparative analysis of TOCO and EHG signals"],"prefix":"10.1088","volume":"12","author":[{"given":"Sahar","family":"Ghorbanzadeh","sequence":"first","affiliation":[{"name":"Islamic Azad University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0793-3797","authenticated-orcid":true,"given":"Mohammad Karimi","family":"Moridani","sequence":"additional","affiliation":[{"name":"Islamic Azad University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2026,9,8]]},"reference":[{"key":"bpexae9ecbbib1","doi-asserted-by":"publisher","first-page":"761","DOI":"10.1016\/j.medengphy.2014.01.009","type":"journal-article","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":"bpexae9ecbbib2","article-title":"Born too soon: the global action report on preterm birth","author":"WHO","year":"2012","type":"report"},{"key":"bpexae9ecbbib3","first-page":"p 436","type":"book","author":"Baker","year":"2011","edition":"19th edn"},{"key":"bpexae9ecbbib4","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1016\/j.siny.2012.01.009","type":"journal-article","article-title":"Long term respiratory outcomes of very premature birth (< 32 weeks)","volume":"17","author":"Greenough","year":"2012","journal-title":"Semin. Fetal Neonatal Med."},{"key":"bpexae9ecbbib5","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1056\/NEJMra0904308","type":"journal-article","article-title":"The enigma of spontaneous preterm birth","volume":"362","author":"Muglia","year":"2010","journal-title":"New Engl. J. Med."},{"key":"bpexae9ecbbib6","doi-asserted-by":"publisher","first-page":"e312","DOI":"10.1542\/peds.2008-1827","type":"journal-article","article-title":"The cost of preterm birth throughout childhood in England and Wales","volume":"123","author":"Mangham","year":"2009","journal-title":"Pediatrics"},{"key":"bpexae9ecbbib7","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1136\/archdischild-2011-301096","type":"journal-article","article-title":"Prevention of preterm births: are we looking in the wrong place? The case for primary prevention","volume":"97","author":"Rattihalli","year":"2012","journal-title":"Arch. Dis. Child. Fetal Neonatal Ed."},{"key":"bpexae9ecbbib8","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1016\/S0301-2115(00)00418-8","type":"journal-article","article-title":"Uterine electrical activity as predictor of preterm birth in women with preterm contractions","volume":"95","author":"Verdenik","year":"2001","journal-title":"Eur. J. Obstet. Gynecol. Reprod. Biol."},{"key":"bpexae9ecbbib9","doi-asserted-by":"publisher","first-page":"1254","DOI":"10.1016\/s0029-7844(03)00341-7","type":"journal-article","article-title":"Predicting term and preterm delivery with transabdominal uterine electromyography","volume":"101","author":"Maner","year":"2003","journal-title":"Obstet. Gynecol."},{"key":"bpexae9ecbbib10","doi-asserted-by":"publisher","first-page":"402","DOI":"10.1016\/S0029-7844(02)02505-X","type":"journal-article","article-title":"Prediction and early detection of preterm labor","volume":"101","author":"Iams","year":"2003","journal-title":"Obstet. Gynecol."},{"key":"bpexae9ecbbib11","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0202125","type":"journal-article","article-title":"Characterization and automatic classification of preterm and term uterine records","volume":"13","author":"Jager","year":"2018","journal-title":"PLoS One"},{"key":"bpexae9ecbbib12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0077154","type":"journal-article","article-title":"Prediction of preterm deliveries from EHG signals using machine learning","volume":"8","author":"Fergus","year":"2013","journal-title":"PLoS One"},{"key":"bpexae9ecbbib13","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1111\/j.1600-0412.2010.01031.x","type":"journal-article","article-title":"Use of uterine electromyography to diagnose term and preterm labor","volume":"90","author":"Lu\u010dovnik","year":"2011","journal-title":"Acta Obstet. Gynecol. Scand."},{"key":"bpexae9ecbbib14","doi-asserted-by":"publisher","first-page":"1325","DOI":"10.1016\/j.ajog.2004.12.055","type":"journal-article","article-title":"The epidemiology of threatened preterm labor: a prospective cohort study","volume":"192","author":"McPheeters","year":"2005","journal-title":"Am. J. Obstet. Gynecol."},{"key":"bpexae9ecbbib15","doi-asserted-by":"publisher","first-page":"1182","DOI":"10.1109\/TBME.1986.325698","type":"journal-article","article-title":"Uterine EHG processing for obstetrical monitoring","volume":"333","author":"Marque","year":"1986","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"bpexae9ecbbib16","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1007\/BF00668252","type":"journal-article","article-title":"Electrical and mechanical activity of the human uterus during labour","volume":"208","author":"Wolfs","year":"1970","journal-title":"Arch. Gyn\u00e4k"},{"key":"bpexae9ecbbib17","doi-asserted-by":"publisher","first-page":"406","DOI":"10.1111\/apha.12424","type":"journal-article","article-title":"Propagation of electrical activity in uterine muscle during pregnancy: a review","volume":"213","author":"Rabotti","year":"2015","journal-title":"Acta Physiol."},{"key":"bpexae9ecbbib18","doi-asserted-by":"publisher","first-page":"S5","DOI":"10.1186\/1471-2393-7-S1-S5","type":"journal-article","article-title":"Preterm labour detection by use of a biophysical marker: the uterine electrical activity","volume":"7","author":"Marque","year":"2007","journal-title":"BMC Pregnancy Childbirth"},{"key":"bpexae9ecbbib19","doi-asserted-by":"publisher","first-page":"103","DOI":"10.5573\/IEIESPC.2015.4.2.103","type":"journal-article","article-title":"Time-frequency analysis of electrohysterogram for classification of term and preterm birth","volume":"4","author":"Ryu","year":"2015","journal-title":"IEIE Trans. Smart Process. Comput."},{"key":"bpexae9ecbbib20","doi-asserted-by":"publisher","first-page":"574","DOI":"10.1016\/j.bbe.2016.06.004","type":"journal-article","article-title":"Early predicting a risk of preterm labour by analysis of antepartum electrohysterograhic signals","volume":"36","author":"Horoba","year":"2016","journal-title":"Biocybern. Biomed. Eng."},{"key":"bpexae9ecbbib21","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1186\/s12884-018-1778-1","type":"journal-article","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"},{"key":"bpexae9ecbbib22","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0214712","type":"journal-article","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":"bpexae9ecbbib23","doi-asserted-by":"publisher","first-page":"3019","DOI":"10.3934\/mbe.2020171","type":"journal-article","article-title":"Critical analysis of electrohysterographic methods for continuous monitoring of intrauterine pressure","volume":"17","author":"Rooijakkers","year":"2020","journal-title":"Math. Biosci. Eng."},{"key":"bpexae9ecbbib24","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-23782-w","type":"journal-article","article-title":"Development of prognostic model for preterm birth using machine learning in a population-based cohort of Western Australia births between 1980 and 2015","volume":"12","author":"Wong","year":"2022","journal-title":"Sci. Rep."},{"key":"bpexae9ecbbib25","doi-asserted-by":"publisher","first-page":"4845","DOI":"10.1038\/s41598-024-55627-z","type":"journal-article","article-title":"Development and validation of a simplified risk prediction model for preterm birth: a prospective cohort study in rural Ethiopia","volume":"14","author":"Kassahun","year":"2024","journal-title":"Sci. Rep."},{"key":"bpexae9ecbbib26","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-60097-4","type":"journal-article","article-title":"Prediction of preterm birth in multiparous women using logistic regression and machine learning approaches","volume":"14","author":"Belaghi","year":"2024","journal-title":"Sci. Rep."},{"key":"bpexae9ecbbib27","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-48466-x","type":"journal-article","article-title":"Predicting preterm birth using machine learning techniques in oral microbiome","volume":"13","author":"Hong","year":"2023","journal-title":"Sci. Rep."},{"key":"bpexae9ecbbib28","doi-asserted-by":"publisher","first-page":"100","DOI":"10.3991\/ijoe.v17i05.11133","type":"journal-article","article-title":"Analysis of heart rate dynamics before and during meditation","volume":"17","author":"Moridani","year":"2021","journal-title":"Int. J. Online Biomed. Eng."},{"key":"bpexae9ecbbib29","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1080\/08820538.2020.1781905","type":"journal-article","article-title":"Time-frequency analysis of photopic negative response in CRVO patients","volume":"35","author":"Behbahani","year":"2020","journal-title":"Semin. Ophthalmol."},{"key":"bpexae9ecbbib30","doi-asserted-by":"publisher","first-page":"619","DOI":"10.4149\/BLL_2020_107","type":"journal-article","article-title":"Heart rate variability features for different stress classification","volume":"121","author":"Moridani","year":"2020","journal-title":"Bratislavske Lekarske Listy"},{"key":"bpexae9ecbbib31","doi-asserted-by":"publisher","first-page":"81","DOI":"10.34028\/iajit\/19\/1\/10","type":"journal-article","article-title":"Designing an intelligent system to detect stress levels during driving","volume":"9","author":"Mohammad","year":"2022","journal-title":"Int. Arab J. Inf. Technol."},{"key":"bpexae9ecbbib32","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1080\/03091902.2022.2026504","type":"journal-article","article-title":"An automated method for sleep apnoea detection using HRV","volume":"46","author":"Karimi Moridani","year":"2022","journal-title":"J. Med. Eng. Technol."},{"key":"bpexae9ecbbib33","doi-asserted-by":"publisher","first-page":"137","DOI":"10.3991\/ijoe.v16i09.14485","type":"journal-article","article-title":"A review of the methods for sudden cardiac death detection: a guide for emergency physicians","volume":"16","author":"Moridani","year":"2020","journal-title":"Int. J. Online Biomed. Eng."},{"key":"bpexae9ecbbib34","doi-asserted-by":"publisher","first-page":"87","DOI":"10.3109\/03091902.2016.1139201","type":"journal-article","article-title":"Non linear feature extraction from HRV signal for mortality prediction of ICU cardiovascular patient","volume":"40","author":"Moridani","year":"2016","journal-title":"J. Med. Eng. Technol."},{"key":"bpexae9ecbbib35","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0330914","type":"journal-article","article-title":"Deep feature engineering for accurate sperm morphology classification using CBAM-enhanced ResNet50","volume":"20","author":"K\u0131l\u0131\u00e7","year":"2025","journal-title":"PLoS One"},{"key":"bpexae9ecbbib36","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.103963","type":"journal-article","article-title":"A new approach for physical human activity recognition from sensor signals based on motif patterns and long-short term memory","volume":"78","author":"Kuncan","year":"2022","journal-title":"Biomed. J."},{"key":"bpexae9ecbbib37","doi-asserted-by":"publisher","first-page":"220364","DOI":"10.1109\/ACCESS.2020.3040649","type":"journal-article","article-title":"Using ResNet transfer deep learning methods in person identification according to physical actions","volume":"8","author":"K\u0131l\u0131\u00e7","year":"2020","journal-title":"IEEE Access"},{"key":"bpexae9ecbbib38","doi-asserted-by":"crossref","DOI":"10.1109\/SMART-TECH49988.2020.00033","type":"conference-proceedings","article-title":"Electrohysterogram based term and preterm delivery classification system","author":"Khan","year":"2020"},{"key":"bpexae9ecbbib39","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-021-81492-1","type":"journal-article","article-title":"Automatic recognition of uterine contractions with electrohysterogram signals based on the zero crossing rate","volume":"11","author":"Song","year":"2021","journal-title":"Sci. Rep."},{"key":"bpexae9ecbbib40","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.bbe.2020.01.007","type":"journal-article","article-title":"Granger causal analysis of electrohysterographic and TOCO graphic recordings for classification of term vs. preterm births","volume":"40","author":"Saleem","year":"2020","journal-title":"Biocybern. Biomed. Eng."},{"key":"bpexae9ecbbib41","doi-asserted-by":"publisher","first-page":"3350","DOI":"10.3390\/s21103350","type":"journal-article","article-title":"Optimized feature subset selection using genetic algorithm for preterm labor prediction based on electrohysterography","volume":"21","author":"Nieto-del-Amor","year":"2021","journal-title":"MDPI"},{"key":"bpexae9ecbbib42","type":"journal-article","article-title":"Term-preterm EHG dataset with tocogram (version 1.0.0)","author":"Jager","year":"2018","journal-title":"PhysioNet"}],"container-title":["Biomedical Physics &amp; Engineering Express"],"original-title":[],"link":[{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2057-1976\/ae9ecb","content-type":"text\/html","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2057-1976\/ae9ecb\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2057-1976\/ae9ecb","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2057-1976\/ae9ecb\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2057-1976\/ae9ecb\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2057-1976\/ae9ecb\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2057-1976\/ae9ecb\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"similarity-checking"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2057-1976\/ae9ecb\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T05:46:52Z","timestamp":1788846412000},"score":1,"resource":{"primary":{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2057-1976\/ae9ecb"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,8]]},"references-count":42,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2026,9,8]]},"published-print":{"date-parts":[[2026,10,1]]}},"URL":"https:\/\/doi.org\/10.1088\/2057-1976\/ae9ecb","relation":{},"ISSN":["2057-1976"],"issn-type":[{"value":"2057-1976","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,8]]},"assertion":[{"value":"A geometric nonlinear return mapping framework for automated term\u2013preterm delivery classification: a comparative analysis of TOCO and EHG signals","name":"article_title","label":"Article Title"},{"value":"Biomedical Physics & Engineering Express","name":"journal_title","label":"Journal Title"},{"value":"paper","name":"article_type","label":"Article Type"},{"value":"\u00a9 2026 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2026-04-23","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-08-26","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-09-08","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}