{"id":"https://openalex.org/W4415933526","doi":"https://doi.org/10.1016/j.bspc.2026.110654","title":"CleanCTG: A Deep Learning Model for Multi-Artefact Detection and Reconstruction in Cardiotocography","display_name":"CleanCTG: A Deep Learning Model for Multi-Artefact Detection and Reconstruction in Cardiotocography","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4415933526","doi":"https://doi.org/10.1016/j.bspc.2026.110654"},"language":"en","primary_location":{"id":"doi:10.1016/j.bspc.2026.110654","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.bspc.2026.110654","pdf_url":null,"source":{"id":"https://openalex.org/S8427965","display_name":"Biomedical Signal Processing and Control","issn_l":"1746-8094","issn":["1746-8094","1746-8108"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Biomedical Signal Processing and Control","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1016/j.bspc.2026.110654","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5021806060","display_name":"Sheng Wong","orcid":"https://orcid.org/0000-0001-7444-1405"},"institutions":[{"id":"https://openalex.org/I40120149","display_name":"University of Oxford","ror":"https://ror.org/052gg0110","country_code":"GB","type":"education","lineage":["https://openalex.org/I40120149"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Sheng Wong","raw_affiliation_strings":["University of Oxford"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Oxford","institution_ids":["https://openalex.org/I40120149"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009106997","display_name":"Beth Albert","orcid":null},"institutions":[{"id":"https://openalex.org/I40120149","display_name":"University of Oxford","ror":"https://ror.org/052gg0110","country_code":"GB","type":"education","lineage":["https://openalex.org/I40120149"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Beth Albert","raw_affiliation_strings":["University of Oxford"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Oxford","institution_ids":["https://openalex.org/I40120149"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008942107","display_name":"Gabriel Davis Jones","orcid":"https://orcid.org/0000-0003-4070-4814"},"institutions":[{"id":"https://openalex.org/I40120149","display_name":"University of Oxford","ror":"https://ror.org/052gg0110","country_code":"GB","type":"education","lineage":["https://openalex.org/I40120149"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Gabriel Davis Jones","raw_affiliation_strings":["University of Oxford"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Oxford","institution_ids":["https://openalex.org/I40120149"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5021806060"],"corresponding_institution_ids":["https://openalex.org/I40120149"],"apc_list":{"value":2420,"currency":"USD","value_usd":2420},"apc_paid":{"value":2420,"currency":"USD","value_usd":2420},"fwci":2.8676,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.92255864,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":"123","issue":null,"first_page":"110654","last_page":"110654"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11184","display_name":"Neonatal and fetal brain pathology","score":0.8788999915122986,"subfield":{"id":"https://openalex.org/subfields/2735","display_name":"Pediatrics, Perinatology and Child Health"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11184","display_name":"Neonatal and fetal brain pathology","score":0.8788999915122986,"subfield":{"id":"https://openalex.org/subfields/2735","display_name":"Pediatrics, Perinatology and Child Health"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T12552","display_name":"Fetal and Pediatric Neurological Disorders","score":0.03739999979734421,"subfield":{"id":"https://openalex.org/subfields/2735","display_name":"Pediatrics, Perinatology and Child Health"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11021","display_name":"ECG Monitoring and Analysis","score":0.03370000049471855,"subfield":{"id":"https://openalex.org/subfields/2705","display_name":"Cardiology and Cardiovascular Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/cardiotocography","display_name":"Cardiotocography","score":0.7562999725341797},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.6859999895095825},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.49470001459121704},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4867999851703644},{"id":"https://openalex.org/keywords/fetal-heart-rate","display_name":"Fetal heart rate","score":0.45669999718666077},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4361000061035156},{"id":"https://openalex.org/keywords/interpolation","display_name":"Interpolation (computer graphics)","score":0.43050000071525574},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.412200003862381}],"concepts":[{"id":"https://openalex.org/C2776046940","wikidata":"https://www.wikidata.org/wiki/Q886292","display_name":"Cardiotocography","level":4,"score":0.7562999725341797},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.6859999895095825},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6237999796867371},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5281999707221985},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.49470001459121704},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4867999851703644},{"id":"https://openalex.org/C3020626262","wikidata":"https://www.wikidata.org/wiki/Q886292","display_name":"Fetal heart rate","level":4,"score":0.45669999718666077},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4361000061035156},{"id":"https://openalex.org/C137800194","wikidata":"https://www.wikidata.org/wiki/Q11713455","display_name":"Interpolation (computer graphics)","level":3,"score":0.43050000071525574},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.412200003862381},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.35269999504089355},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3303999900817871},{"id":"https://openalex.org/C40969351","wikidata":"https://www.wikidata.org/wiki/Q3516228","display_name":"Word error rate","level":2,"score":0.3301999866962433},{"id":"https://openalex.org/C159693508","wikidata":"https://www.wikidata.org/wiki/Q3301075","display_name":"Phonocardiogram","level":2,"score":0.3230000138282776},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.2865000069141388},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.28610000014305115},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.27129998803138733},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.2703000009059906},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.26600000262260437},{"id":"https://openalex.org/C175079658","wikidata":"https://www.wikidata.org/wiki/Q7312165","display_name":"Remote patient monitoring","level":2,"score":0.26489999890327454},{"id":"https://openalex.org/C10551718","wikidata":"https://www.wikidata.org/wiki/Q5227332","display_name":"Data pre-processing","level":2,"score":0.2623000144958496},{"id":"https://openalex.org/C171836373","wikidata":"https://www.wikidata.org/wiki/Q2266329","display_name":"Linear interpolation","level":3,"score":0.2590000033378601},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.2583000063896179},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.2515000104904175}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1016/j.bspc.2026.110654","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.bspc.2026.110654","pdf_url":null,"source":{"id":"https://openalex.org/S8427965","display_name":"Biomedical Signal Processing and Control","issn_l":"1746-8094","issn":["1746-8094","1746-8108"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Biomedical Signal Processing and Control","raw_type":"journal-article"},{"id":"doi:10.2139/ssrn.5699947","is_oa":true,"landing_page_url":"https://doi.org/10.2139/ssrn.5699947","pdf_url":null,"source":{"id":"https://openalex.org/S4210172589","display_name":"SSRN Electronic Journal","issn_l":"1556-5068","issn":["1556-5068"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1318003438","host_organization_name":"RELX Group (Netherlands)","host_organization_lineage":["https://openalex.org/I1318003438"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"posted-content"}],"best_oa_location":{"id":"doi:10.1016/j.bspc.2026.110654","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.bspc.2026.110654","pdf_url":null,"source":{"id":"https://openalex.org/S8427965","display_name":"Biomedical Signal Processing and Control","issn_l":"1746-8094","issn":["1746-8094","1746-8108"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Biomedical Signal Processing and Control","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320334626","display_name":"Medical Research Council","ror":"https://ror.org/03x94j517"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W842322697","https://openalex.org/W1807000996","https://openalex.org/W2014919872","https://openalex.org/W2064141848","https://openalex.org/W2112246101","https://openalex.org/W2160033581","https://openalex.org/W2493957679","https://openalex.org/W2774072613","https://openalex.org/W2994832884","https://openalex.org/W3027457525","https://openalex.org/W3079819599","https://openalex.org/W3118973727","https://openalex.org/W3175239358","https://openalex.org/W3204549297","https://openalex.org/W4210307369","https://openalex.org/W4293414947","https://openalex.org/W4295679762","https://openalex.org/W4306964096","https://openalex.org/W4311773132","https://openalex.org/W4311988661","https://openalex.org/W4323316167","https://openalex.org/W4327521640","https://openalex.org/W4364353524","https://openalex.org/W4380421892","https://openalex.org/W4380680744","https://openalex.org/W4393868964","https://openalex.org/W4394764395","https://openalex.org/W4406084790","https://openalex.org/W4407112833","https://openalex.org/W4409669232","https://openalex.org/W4410008596","https://openalex.org/W4411034183"],"related_works":[],"abstract_inverted_index":{"Cardiotocography":[0],"(CTG)":[1],"is":[2,8],"essential":[3],"for":[4],"fetal":[5,31],"monitoring":[6,232],"but":[7],"frequently":[9],"compromised":[10],"by":[11,161,212],"diverse":[12],"artefacts,":[13],"including":[14],"beat":[15],"halving":[16],"and":[17,25,36,76,99,137,206,221,229],"doubling,":[18],"maternal":[19],"heart":[20,32],"rate":[21,33],"overlap,":[22],"missing":[23,74],"segments":[24,105,145,172],"isolated":[26],"spikes,":[27],"which":[28],"obscure":[29],"true":[30],"(FHR)":[34],"patterns":[35],"can":[37,224],"lead":[38],"to":[39,78,146,204,210,238],"misdiagnosis":[40],"or":[41,55,68],"delayed":[42],"intervention.":[43],"Current":[44],"deep\u2010learning":[45],"approaches":[46],"typically":[47],"bypass":[48],"comprehensive":[49],"noise":[50],"handling,":[51],"applying":[52],"minimal":[53],"preprocessing":[54],"focusing":[56],"solely":[57],"on":[58,65,143,167,193],"downstream":[59],"classification,":[60],"while":[61],"traditional":[62],"methods":[63],"rely":[64],"simple":[66],"interpolation":[67],"rule\u2010based":[69],"filtering":[70],"that":[71,90,217],"addresses":[72],"only":[73],"samples":[75],"fail":[77],"correct":[79],"complex":[80],"artefact":[81,94,132,219],"types.":[82],"We":[83],"present":[84],"CleanCTG,":[85],"an":[86],"end\u2010to\u2010end":[87],"dual\u2010stage":[88],"model":[89],"first":[91],"identifies":[92],"multiple":[93],"types":[95],"via":[96],"multi\u2010scale":[97],"convolution":[98],"context\u2010aware":[100],"cross\u2010attention,":[101],"then":[102],"reconstructs":[103],"corrupted":[104,119,144],"through":[106],"artefact\u2010specific":[107],"correction":[108],"branches.":[109],"Training":[110],"utilised":[111],"over":[112],"800,000":[113],"minutes":[114,169],"of":[115,170],"physiologically":[116],"realistic,":[117],"synthetically":[118],"CTGs":[120],"derived":[121],"from":[122],"expert\u2010verified":[123],"\u201cclean\u201d":[124],"recordings.":[125],"On":[126],"synthetic":[127],"data,":[128],"CleanCTG":[129],"achieved":[130],"perfect":[131],"detection":[133],"(AU-ROC":[134],"=":[135,152,175,178],"1.00)":[136],"reduced":[138],"mean":[139],"squared":[140],"error":[141],"(MSE)":[142],"2.74":[147],"\u00d7":[148,154],"10\u22124":[149],"(clean\u2010segment":[150],"MSE":[151],"2.40":[153],"10\u22126),":[155],"outperforming":[156],"the":[157,190],"next":[158],"best":[159],"method":[160],"more":[162,239],"than":[163],"60%.":[164],"External":[165],"validation":[166],"10,230":[168],"clinician\u2010annotated":[171],"yielded":[173],"AU-ROC":[174],"0.95":[176],"(sensitivity":[177],"83.44%,":[179],"specificity":[180,201],"94.22%),":[181],"surpassing":[182],"six":[183],"comparator":[184],"classifiers.":[185],"Finally,":[186],"when":[187],"integrated":[188],"with":[189],"Dawes-Redman\u2122":[191],"system":[192],"900":[194],"clinical":[195],"CTG":[196,241],"recordings,":[197],"denoised":[198],"traces":[199],"increased":[200],"(from":[202],"80.70%":[203],"82.70%)":[205],"shortened":[207],"median":[208],"time":[209],"decision":[211],"33%.":[213],"These":[214],"findings":[215],"suggest":[216],"explicit":[218],"removal":[220],"signal":[222],"reconstruction":[223],"both":[225],"maintain":[226],"diagnostic":[227],"accuracy":[228],"enable":[230],"shorter":[231],"sessions,":[233],"offering":[234],"a":[235],"practical":[236],"route":[237],"reliable":[240],"interpretation.":[242]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-05-21T09:19:25.381259","created_date":"2025-11-05T00:00:00"}