{"id":"https://openalex.org/W4315629674","doi":"https://doi.org/10.1109/globecom48099.2022.10000857","title":"Unsupervised Representation Learning-based Doppler Ultrasound Signal Quality Assessment","display_name":"Unsupervised Representation Learning-based Doppler Ultrasound Signal Quality Assessment","publication_year":2022,"publication_date":"2022-12-04","ids":{"openalex":"https://openalex.org/W4315629674","doi":"https://doi.org/10.1109/globecom48099.2022.10000857"},"language":"en","primary_location":{"id":"doi:10.1109/globecom48099.2022.10000857","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/globecom48099.2022.10000857","pdf_url":null,"source":{"id":"https://openalex.org/S4363607705","display_name":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5026957327","display_name":"Xintong Shi","orcid":"https://orcid.org/0000-0001-5094-3005"},"institutions":[{"id":"https://openalex.org/I203951103","display_name":"Keio University","ror":"https://ror.org/02kn6nx58","country_code":"JP","type":"education","lineage":["https://openalex.org/I203951103"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Xintong Shi","raw_affiliation_strings":["Keio University,Department of Information and Computer Science,Kanagawa,Japan,223\u20138522"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Keio University,Department of Information and Computer Science,Kanagawa,Japan,223\u20138522","institution_ids":["https://openalex.org/I203951103"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008604168","display_name":"Kohei Yamamoto","orcid":"https://orcid.org/0000-0001-9669-3566"},"institutions":[{"id":"https://openalex.org/I203951103","display_name":"Keio University","ror":"https://ror.org/02kn6nx58","country_code":"JP","type":"education","lineage":["https://openalex.org/I203951103"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kohei Yamamoto","raw_affiliation_strings":["Keio University,Department of Information and Computer Science,Kanagawa,Japan,223\u20138522"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Keio University,Department of Information and Computer Science,Kanagawa,Japan,223\u20138522","institution_ids":["https://openalex.org/I203951103"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016337773","display_name":"Tomoaki Ohtsuki","orcid":"https://orcid.org/0000-0003-3961-1426"},"institutions":[{"id":"https://openalex.org/I203951103","display_name":"Keio University","ror":"https://ror.org/02kn6nx58","country_code":"JP","type":"education","lineage":["https://openalex.org/I203951103"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Tomoaki Ohtsuki","raw_affiliation_strings":["Keio University,Department of Information and Computer Science,Kanagawa,Japan,223\u20138522"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Keio University,Department of Information and Computer Science,Kanagawa,Japan,223\u20138522","institution_ids":["https://openalex.org/I203951103"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031545939","display_name":"Yutaka Matsui","orcid":null},"institutions":[{"id":"https://openalex.org/I4210091528","display_name":"Atom Medical (Japan)","ror":"https://ror.org/00cvsxf87","country_code":"JP","type":"company","lineage":["https://openalex.org/I4210091528"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yutaka Matsui","raw_affiliation_strings":["Atom Medical Co. Ltd,Research &#x0026; Development Group Technical Department,Tokyo,Japan,113\u20130021"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Atom Medical Co. Ltd,Research &#x0026; Development Group Technical Department,Tokyo,Japan,113\u20130021","institution_ids":["https://openalex.org/I4210091528"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5113897218","display_name":"Kazunari Owada","orcid":null},"institutions":[{"id":"https://openalex.org/I4210091528","display_name":"Atom Medical (Japan)","ror":"https://ror.org/00cvsxf87","country_code":"JP","type":"company","lineage":["https://openalex.org/I4210091528"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kazunari Owada","raw_affiliation_strings":["Atom Medical Co. Ltd,Research &#x0026; Development Group Technical Department,Tokyo,Japan,113\u20130021"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Atom Medical Co. Ltd,Research &#x0026; Development Group Technical Department,Tokyo,Japan,113\u20130021","institution_ids":["https://openalex.org/I4210091528"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.2061,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.82627119,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"abs 1312 6114","issue":null,"first_page":"2254","last_page":"2259"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11184","display_name":"Neonatal and fetal brain pathology","score":0.9972000122070312,"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":0.9972000122070312,"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/T12419","display_name":"Phonocardiography and Auscultation Techniques","score":0.9904000163078308,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory Medicine"},"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":0.9891999959945679,"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/computer-science","display_name":"Computer science","score":0.6465447545051575},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.6095205545425415},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5392467379570007},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.5348343849182129},{"id":"https://openalex.org/keywords/kalman-filter","display_name":"Kalman filter","score":0.5074886679649353},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5022494792938232},{"id":"https://openalex.org/keywords/doppler-effect","display_name":"Doppler effect","score":0.4172605872154236},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4138791859149933},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.35062742233276367},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.26431626081466675},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.21798241138458252},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.139267235994339}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6465447545051575},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.6095205545425415},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5392467379570007},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.5348343849182129},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.5074886679649353},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5022494792938232},{"id":"https://openalex.org/C142757262","wikidata":"https://www.wikidata.org/wiki/Q76436","display_name":"Doppler effect","level":2,"score":0.4172605872154236},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4138791859149933},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.35062742233276367},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.26431626081466675},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.21798241138458252},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.139267235994339},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/globecom48099.2022.10000857","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/globecom48099.2022.10000857","pdf_url":null,"source":{"id":"https://openalex.org/S4363607705","display_name":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5400000214576721,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1539380049","https://openalex.org/W2021044602","https://openalex.org/W2105934661","https://openalex.org/W2159543312","https://openalex.org/W2289602645","https://openalex.org/W2591815631","https://openalex.org/W2739083426","https://openalex.org/W2799246531","https://openalex.org/W2800844760","https://openalex.org/W2896655658","https://openalex.org/W2911847574","https://openalex.org/W2931608861","https://openalex.org/W2945801048","https://openalex.org/W2965957910","https://openalex.org/W2976710966","https://openalex.org/W3045375480","https://openalex.org/W3111412575","https://openalex.org/W3197046559","https://openalex.org/W6640963894","https://openalex.org/W6763010256"],"related_works":["https://openalex.org/W3013693939","https://openalex.org/W2566616303","https://openalex.org/W2159052453","https://openalex.org/W3131327266","https://openalex.org/W2734887215","https://openalex.org/W4297051394","https://openalex.org/W2752972570","https://openalex.org/W4386815338","https://openalex.org/W2145836866","https://openalex.org/W2803255133"],"abstract_inverted_index":{"The":[0,197],"Doppler":[1],"ultrasound":[2],"(DUS)":[3],"transducer":[4],"has":[5],"been":[6],"widely":[7],"used":[8],"for":[9,75,137,190],"fetal":[10,17,34,41,76,98,122,134,150,186],"heart":[11],"rate":[12],"(FHR)":[13],"monitoring.":[14],"However,":[15],"the":[16,21,65,97,138,176,192,207],"DUS":[18,42,46,77,99,123,135,151],"signals":[19,43,62],"from":[20,60,153],"transducers":[22],"can":[23,51],"be":[24],"corrupted":[25],"by":[26],"several":[27],"interference":[28],"sources":[29],"such":[30],"as":[31,162],"maternal":[32],"and":[33,79,93,106,157,179,216],"movements,":[35],"which":[36],"makes":[37],"FHR":[38,68,141,195],"estimation":[39,142],"using":[40,168],"challenging.":[44],"Fetal":[45],"signal":[47,119,164],"quality":[48,120,165],"assessment":[49],"(SQA)":[50],"help":[52],"to":[53,63,118,184],"remove":[54],"or":[55],"interpolate":[56],"unreliable":[57],"FHRs":[58],"estimated":[59],"noisy":[61],"improve":[64],"accuracy":[66,86],"of":[67,81,140,148,194,214,221],"estimation.":[69,196],"There":[70],"are":[71,87,104],"some":[72],"existing":[73],"approaches":[74,83],"SQA,":[78],"most":[80],"these":[82,160],"with":[84,101],"high":[85],"based":[88],"on":[89,112],"supervised":[90],"learning-based":[91,133],"algorithms":[92],"human-defined":[94,107],"properties.":[95],"Nonetheless,":[96],"datasets":[100],"quality-level":[102],"annotations":[103],"limited,":[105],"properties":[108],"place":[109],"a":[110,169,180],"limitation":[111],"mining":[113],"more":[114],"deep":[115],"information":[116],"related":[117],"in":[121],"signals.":[124],"In":[125],"this":[126],"paper,":[127],"we":[128,174],"propose":[129],"an":[130],"unsupervised":[131],"representation":[132],"SQA":[136],"improvement":[139],"performance.":[143],"We":[144],"firstly":[145],"learn":[146],"representations":[147,161],"pre-processed":[149],"data":[152],"variational":[154],"autoencoder":[155],"(VAE)":[156],"then":[158],"combine":[159],"one":[163],"index":[166],"(SQI)":[167],"self-organizing":[170],"map":[171],"(SOM).":[172],"Finally,":[173],"apply":[175],"combined":[177],"SQI":[178],"Kalman":[181],"filter":[182],"(KF)":[183],"estimate":[185],"RR":[187],"intervals":[188],"(FRRI)":[189],"reducing":[191],"errors":[193],"experimental":[198],"results":[199],"showed":[200],"that":[201],"our":[202],"proposed":[203],"method":[204],"could":[205],"reduce":[206],"averaged":[208,217],"root":[209],"mean":[210],"squared":[211],"error":[212,219],"(RMSE)":[213],"FRRI":[215],"absolute":[218],"(AAE)":[220],"FHR.":[222]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
