{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T16:20:27Z","timestamp":1783182027720,"version":"3.54.6"},"reference-count":47,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T00:00:00Z","timestamp":1779235200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100011033","name":"Agencia Estatal de Investigaci\u00f3n","doi-asserted-by":"publisher","award":["DIN2021-012073"],"award-info":[{"award-number":["DIN2021-012073"]}],"id":[{"id":"10.13039\/501100011033","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100011033","name":"Agencia Estatal de Investigaci\u00f3n","doi-asserted-by":"publisher","award":["PTQ2024-013671"],"award-info":[{"award-number":["PTQ2024-013671"]}],"id":[{"id":"10.13039\/501100011033","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100011033","name":"Agencia Estatal de Investigaci\u00f3n","doi-asserted-by":"publisher","award":["AEI\/10.13039\/501100011033"],"award-info":[{"award-number":["AEI\/10.13039\/501100011033"]}],"id":[{"id":"10.13039\/501100011033","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014440","name":"Spain Ministry of Science Innovation and Universities","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100014440","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000780","name":"European Commission","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004895","name":"European Social Fund Plus","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004895","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004233","name":"Polytechnic University of Valencia","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004233","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computer Methods and Programs in Biomedicine"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.cmpb.2026.109453","type":"journal-article","created":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T23:23:28Z","timestamp":1779319408000},"page":"109453","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Deep learning-driven automatic segmentation of uterine peristalsis in intracavitary electrohysterography signals"],"prefix":"10.1016","volume":"284","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-7899-4864","authenticated-orcid":false,"given":"Juan Miguel","family":"Mira-Tomas","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4605-6048","authenticated-orcid":false,"given":"Alba","family":"Diaz-Martinez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jose","family":"Alberola-Rubio","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joaqu\u00edn","family":"Llacer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gemma","family":"Castill\u00f3n","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1020-0239","authenticated-orcid":false,"given":"Sergio","family":"Caballero","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pilar","family":"Alama","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1410-2721","authenticated-orcid":false,"given":"Javier","family":"Garcia-Casado","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.cmpb.2026.109453_b1","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6579\/aaad56","article-title":"Electrohysterography in the diagnosis of preterm birth: a review","volume":"39","author":"Garcia-Casado","year":"2018","journal-title":"Physiol. Meas."},{"key":"10.1016\/j.cmpb.2026.109453_b2","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1111\/j.1749-6632.2001.tb03801.x","article-title":"Contractility of the nonpregnant uterus","volume":"943","author":"Ziegler","year":"2001","journal-title":"Ann. New York Acad. Sci."},{"key":"10.1016\/j.cmpb.2026.109453_b3","first-page":"1469","article-title":"Ultrastructural features of endometrial-myometrial interface and its alteration in adenomyosis","volume":"7","author":"Zhang","year":"2014","journal-title":"Int. J. Clin. Exp. Pathol."},{"key":"10.1016\/j.cmpb.2026.109453_b4","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.rbmo.2017.03.019","article-title":"Uterine peristalsis and fertility: current knowledge and future perspectives: a review and meta-analysis","volume":"35","author":"Kuijsters","year":"2017","journal-title":"Reprod. BioMedicine Online"},{"key":"10.1016\/j.cmpb.2026.109453_b5","doi-asserted-by":"crossref","first-page":"666","DOI":"10.1002\/ijgo.15299","article-title":"Uterine peristalsis in women affected by adenomyosis: A step towards functional assessment","volume":"165","author":"Arena","year":"2024","journal-title":"Int. J. Gynecol. Obs."},{"key":"10.1016\/j.cmpb.2026.109453_b6","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1002\/ijgo.70002","article-title":"Uterine peristalsis of adenomyosis patients on cine magnetic resonance imaging","volume":"170","author":"Kido","year":"2025","journal-title":"Int. J. Gynecol. Obs."},{"key":"10.1016\/j.cmpb.2026.109453_b7","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/j.fertnstert.2012.08.038","article-title":"Dysmenorrhea and its severity are associated with increased uterine contractility and overexpression of oxytocin receptor (OTR) in women with symptomatic adenomyosis","volume":"99","author":"Guo","year":"2013","journal-title":"Fertil. Steril."},{"key":"10.1016\/j.cmpb.2026.109453_b8","doi-asserted-by":"crossref","first-page":"1542","DOI":"10.1093\/oxfordjournals.humrep.a019435","article-title":"Uterine hyperperistalsis and dysperistalsis as dysfunctions of the mechanism of rapid sperm transport in patients with endometriosis and infertility","volume":"11","author":"Leyendecker","year":"1996","journal-title":"Hum. Reprod."},{"key":"10.1016\/j.cmpb.2026.109453_b9","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.ejrad.2004.01.009","article-title":"Uterine contractions evaluated on cine MR imaging in patients with uterine leiomyomas","volume":"53","author":"Nishino","year":"2005","journal-title":"Eur. J. Radiol."},{"key":"10.1016\/j.cmpb.2026.109453_b10","article-title":"Oxytocin antagonists for assisted reproduction","volume":"9","author":"Craciunas","year":"2021","journal-title":"Cochrane Database Syst. Rev."},{"key":"10.1016\/j.cmpb.2026.109453_b11","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1093\/humrep\/dei295","article-title":"Contractile reactivity of human myometrium in isolated non-pregnant uteri","volume":"21","author":"Richter","year":"2006","journal-title":"Hum. Reprod."},{"key":"10.1016\/j.cmpb.2026.109453_b12","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.rbmo.2022.08.104","article-title":"Uterine contractile activity in healthy women throughout the menstrual cycle measured using a novel quantitative two-dimensional transvaginal ultrasound speckle tracking method","volume":"46","author":"Rees","year":"2023","journal-title":"Reprod. BioMedicine Online"},{"key":"10.1016\/j.cmpb.2026.109453_b13","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1007\/s12522-016-0235-y","article-title":"Uterine anatomy and function on cine magnetic resonance imaging","volume":"15","author":"Kido","year":"2016","journal-title":"Reprod. Med. Biology"},{"key":"10.1016\/j.cmpb.2026.109453_b14","first-page":"1","article-title":"Noninvasive imaging of 4D electrical activation patterns of uterine peristalsis during normal menstrual cycles","volume":"2","author":"Wang","year":"2024","journal-title":"Npj Women\u2019s Health 2024 2:1"},{"key":"10.1016\/j.cmpb.2026.109453_b15","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/S0301-2115(97)02727-9","article-title":"Electrohysterogram: Study of the electromechanical activity of the uterus in humans","volume":"73","author":"Shafik","year":"1997","journal-title":"Eur. J. Obs. Gynecol. Reprod. Biology"},{"key":"10.1016\/j.cmpb.2026.109453_b16","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.ejogrb.2025.03.042","article-title":"Intracavitary-electrohysterogram: Novel technique for assessing uterine peristalsis and electrophysiology in non-pregnant women","volume":"309","author":"Alberola-Rubio","year":"2025","journal-title":"Eur. J. Obs. Gynecol. Reprod. Biology"},{"key":"10.1016\/j.cmpb.2026.109453_b17","article-title":"Objective and comprehensive characterization of uterine peristaltic activity throughout the menstrual cycle by means of intracavitary electrohysterography, a cohort study","author":"Alberola-Rubio","year":"2025","journal-title":"Acta Obs. et Gynecol. Scand."},{"key":"10.1016\/j.cmpb.2026.109453_b18","doi-asserted-by":"crossref","DOI":"10.1016\/j.rbmo.2025.105282","article-title":"Quantitative characterization of uterine peristalsis propagation throughout the menstrual cycle using a novel intracavitary electrohysterography method","volume":"52","author":"Imaz-Higuera","year":"2026","journal-title":"Reprod. BioMedicine Online"},{"key":"10.1016\/j.cmpb.2026.109453_b19","doi-asserted-by":"crossref","first-page":"2467","DOI":"10.3390\/s20092467","article-title":"Real-time hand gesture recognition using surface electromyography and machine learning: A systematic literature review","volume":"20","author":"Jaramillo-Y\u00e1nez","year":"2020","journal-title":"Sensors"},{"key":"10.1016\/j.cmpb.2026.109453_b20","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2021.103231","article-title":"Review on EHG signal analysis and its application in preterm diagnosis","volume":"71","author":"Xu","year":"2022","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.cmpb.2026.109453_b21","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.icte.2020.04.004","article-title":"Supervised ECG wave segmentation using convolutional LSTM","volume":"6","author":"Malali","year":"2020","journal-title":"ICT Express"},{"key":"10.1016\/j.cmpb.2026.109453_b22","doi-asserted-by":"crossref","DOI":"10.3390\/ijerph18115780","article-title":"Epileptic seizures detection using deep learning techniques: A review","volume":"18","author":"Shoeibi","year":"2021","journal-title":"Int. J. Environ. Res. Public Health"},{"key":"10.1016\/j.cmpb.2026.109453_b23","series-title":"2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC\u201910","first-page":"2387","article-title":"Nonlinearity of EHG signals used to distinguish active labor from normal pregnancy contractions","author":"Hassan","year":"2010"},{"key":"10.1016\/j.cmpb.2026.109453_b24","series-title":"U-time: A fully convolutional network for time series segmentation applied to sleep staging","author":"Perslev","year":"2019"},{"key":"10.1016\/j.cmpb.2026.109453_b25","series-title":"An empirical evaluation of generic convolutional and recurrent networks for sequence modeling","author":"Bai","year":"2018"},{"key":"10.1016\/j.cmpb.2026.109453_b26","doi-asserted-by":"crossref","first-page":"985","DOI":"10.3390\/electronics12040985","article-title":"Effects of image size on deep learning","volume":"12","author":"Rukundo","year":"2023","journal-title":"Electronics"},{"key":"10.1016\/j.cmpb.2026.109453_b27","series-title":"Focal loss for dense object detection","author":"Lin","year":"2018"},{"key":"10.1016\/j.cmpb.2026.109453_b28","doi-asserted-by":"crossref","DOI":"10.1016\/j.applthermaleng.2023.121545","article-title":"Filling time-series gaps using image techniques: Multidimensional context autoencoder approach for building energy data imputation","volume":"236","author":"Fu","year":"2024","journal-title":"Appl. Therm. Eng."},{"key":"10.1016\/j.cmpb.2026.109453_b29","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1037\/1082-989X.1.1.30","article-title":"Forming inferences about some intraclass correlation coefficients","volume":"1","author":"McGraw","year":"1996","journal-title":"Psychol. Methods"},{"key":"10.1016\/j.cmpb.2026.109453_b30","first-page":"599","article-title":"Classification model evaluation metrics","volume":"Volume 12","author":"Vujovic","year":"2021","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"10.1016\/j.cmpb.2026.109453_b31","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1186\/s13104-022-06096-y","article-title":"Towards a guideline for evaluation metrics in medical image segmentation","volume":"15","author":"M\u00fcller","year":"2022","journal-title":"BMC Res. Notes"},{"key":"10.1016\/j.cmpb.2026.109453_b32","first-page":"609","article-title":"Automatic monodimensional EHG contractions\u2019 segmentation","volume":"16","author":"Zaylaa","year":"2019","journal-title":"Int. Arab. J. Inf. Technol."},{"key":"10.1016\/j.cmpb.2026.109453_b33","doi-asserted-by":"crossref","first-page":"7014","DOI":"10.3390\/app10207014","article-title":"Automatic contraction detection using uterine electromyography","volume":"10","author":"Esgalhado","year":"2020","journal-title":"Appl. Sci."},{"key":"10.1016\/j.cmpb.2026.109453_b34","series-title":"2020 IEEE 5th Middle East and Africa Conference on Biomedical Engineering","first-page":"1","article-title":"Wavelet and over-segmentation elimination techniques effect on automatic uterine EMG segmentation performance \/ quality","author":"Zaylaa","year":"2020"},{"key":"10.1016\/j.cmpb.2026.109453_b35","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2023.105840","article-title":"Automatic detection and characterization of uterine contraction using electrohysterography","volume":"90","author":"Chen","year":"2024","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.cmpb.2026.109453_b36","article-title":"An automatic classification approach for preterm delivery detection based on deep learning","volume":"84","author":"Rao","year":"2023","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.cmpb.2026.109453_b37","doi-asserted-by":"crossref","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":"10.1016\/j.cmpb.2026.109453_b38","article-title":"Automatic semantic segmentation of EHG recordings by deep learning: An approach to a screening tool for use in clinical practice","volume":"254","author":"del Amor","year":"2024","journal-title":"Comput. Methods Programs Biomed."},{"key":"10.1016\/j.cmpb.2026.109453_b39","unstructured":"T. Kim, J. Kim, Y. Tae, C. Park, J.-H. Choi, J. Choo, Reversible instance normalization for accurate time-series forecasting against distribution shift, in: International Conference on Learning Representations, 2022."},{"key":"10.1016\/j.cmpb.2026.109453_b40","series-title":"Instance normalization: The missing ingredient for fast stylization","author":"Ulyanov","year":"2017"},{"key":"10.1016\/j.cmpb.2026.109453_b41","doi-asserted-by":"crossref","DOI":"10.3389\/fnins.2024.1373515","article-title":"Data leakage in deep learning studies of translational EEG","volume":"18","author":"Brookshire","year":"2024","journal-title":"Front. Neurosci."},{"key":"10.1016\/j.cmpb.2026.109453_b42","doi-asserted-by":"crossref","first-page":"1133","DOI":"10.1109\/TBME.2019.2930618","article-title":"An automatic method for the segmentation and classification of imminent labor contraction from electrohysterograms","volume":"67","author":"Tylcz","year":"2020","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"10.1016\/j.cmpb.2026.109453_b43","doi-asserted-by":"crossref","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"},{"key":"10.1016\/j.cmpb.2026.109453_b44","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1093\/humupd\/dmg011","article-title":"Endometrial wave-like activity in the non-pregnant uterus","volume":"9","author":"Gestel","year":"2003","journal-title":"Hum. Reprod. Update"},{"key":"10.1016\/j.cmpb.2026.109453_b45","doi-asserted-by":"crossref","first-page":"1529","DOI":"10.3390\/rs15061529","article-title":"3D-UNet-LSTM: A deep learning-based radar echo extrapolation model for convective nowcasting","volume":"15","author":"Guo","year":"2023","journal-title":"Remote. Sens."},{"key":"10.1016\/j.cmpb.2026.109453_b46","series-title":"Conference on Computer Vision and Pattern Recognition","first-page":"10418","article-title":"Vits for SITS: Vision transformers for satellite image time series","author":"Tarasiou","year":"2023"},{"key":"10.1016\/j.cmpb.2026.109453_b47","series-title":"2022 IEEE Sensors Applications Symposium","first-page":"1","article-title":"Interpretable CNN for single-channel artifacts detection in raw eeg signals","author":"Paissan","year":"2022"}],"container-title":["Computer Methods and Programs in Biomedicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0169260726002075?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0169260726002075?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T15:59:54Z","timestamp":1783180794000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0169260726002075"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":47,"alternative-id":["S0169260726002075"],"URL":"https:\/\/doi.org\/10.1016\/j.cmpb.2026.109453","relation":{},"ISSN":["0169-2607"],"issn-type":[{"value":"0169-2607","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Deep learning-driven automatic segmentation of uterine peristalsis in intracavitary electrohysterography signals","name":"articletitle","label":"Article Title"},{"value":"Computer Methods and Programs in Biomedicine","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.cmpb.2026.109453","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"109453"}}