{"id":"https://openalex.org/W4387871017","doi":"https://doi.org/10.1109/icc45041.2023.10278863","title":"Arterial Blood Pressure Waveform Estimation from Photoplethysmogram Under Inter-Subject Paradigm Using Subject-Distinguishable Dataset by U-Net and Domain Adversarial Training","display_name":"Arterial Blood Pressure Waveform Estimation from Photoplethysmogram Under Inter-Subject Paradigm Using Subject-Distinguishable Dataset by U-Net and Domain Adversarial Training","publication_year":2023,"publication_date":"2023-05-28","ids":{"openalex":"https://openalex.org/W4387871017","doi":"https://doi.org/10.1109/icc45041.2023.10278863"},"language":"en","primary_location":{"id":"doi:10.1109/icc45041.2023.10278863","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icc45041.2023.10278863","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICC 2023 - IEEE International Conference on Communications","raw_type":"proceedings-article"},"type":"conference-paper","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/A5043200004","display_name":"Rikuto Yoshizawa","orcid":"https://orcid.org/0009-0001-9191-3715"},"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":"Rikuto Yoshizawa","raw_affiliation_strings":["Graduate School of Science and Technology, Keio University,Kanagawa,Japan","Graduate School of Science and Technology, Keio University, Kanagawa, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Science and Technology, Keio University,Kanagawa,Japan","institution_ids":["https://openalex.org/I203951103"]},{"raw_affiliation_string":"Graduate School of Science and Technology, Keio University, Kanagawa, Japan","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","Department of Information and Computer Science, Keio University, Kanagawa, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Keio University,Department of Information and Computer Science,Kanagawa,Japan","institution_ids":["https://openalex.org/I203951103"]},{"raw_affiliation_string":"Department of Information and Computer Science, Keio University, Kanagawa, Japan","institution_ids":["https://openalex.org/I203951103"]}]},{"author_position":"last","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","Department of Information and Computer Science, Keio University, Kanagawa, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Keio University,Department of Information and Computer Science,Kanagawa,Japan","institution_ids":["https://openalex.org/I203951103"]},{"raw_affiliation_string":"Department of Information and Computer Science, Keio University, Kanagawa, Japan","institution_ids":["https://openalex.org/I203951103"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I203951103"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"11","issue":null,"first_page":"3401","last_page":"3406"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11196","display_name":"Non-Invasive Vital Sign Monitoring","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11196","display_name":"Non-Invasive Vital Sign Monitoring","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11700","display_name":"Hemodynamic Monitoring and Therapy","score":0.9983999729156494,"subfield":{"id":"https://openalex.org/subfields/2746","display_name":"Surgery"},"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/T10745","display_name":"Heart Rate Variability and Autonomic Control","score":0.9969000220298767,"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/photoplethysmogram","display_name":"Photoplethysmogram","score":0.7419899702072144},{"id":"https://openalex.org/keywords/generalizability-theory","display_name":"Generalizability theory","score":0.7110666036605835},{"id":"https://openalex.org/keywords/blood-pressure","display_name":"Blood pressure","score":0.6344234347343445},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6141757369041443},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5449009537696838},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4295305609703064},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3995620012283325},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3410514295101166},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2960216999053955},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.20944377779960632},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.14751920104026794},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.14221978187561035}],"concepts":[{"id":"https://openalex.org/C116390426","wikidata":"https://www.wikidata.org/wiki/Q7187885","display_name":"Photoplethysmogram","level":3,"score":0.7419899702072144},{"id":"https://openalex.org/C27158222","wikidata":"https://www.wikidata.org/wiki/Q5532422","display_name":"Generalizability theory","level":2,"score":0.7110666036605835},{"id":"https://openalex.org/C84393581","wikidata":"https://www.wikidata.org/wiki/Q82642","display_name":"Blood pressure","level":2,"score":0.6344234347343445},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6141757369041443},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5449009537696838},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4295305609703064},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3995620012283325},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3410514295101166},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2960216999053955},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.20944377779960632},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.14751920104026794},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.14221978187561035},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icc45041.2023.10278863","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icc45041.2023.10278863","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICC 2023 - IEEE International Conference on Communications","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3","score":0.5199999809265137}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1731081199","https://openalex.org/W1901129140","https://openalex.org/W2194775991","https://openalex.org/W2892314032","https://openalex.org/W2965846681","https://openalex.org/W2979877842","https://openalex.org/W2997346981","https://openalex.org/W3015465660","https://openalex.org/W3023858750","https://openalex.org/W3045199277","https://openalex.org/W3109625451","https://openalex.org/W3120015513","https://openalex.org/W3135184486","https://openalex.org/W3154858758","https://openalex.org/W3180746518","https://openalex.org/W3186099984","https://openalex.org/W3198128029","https://openalex.org/W4200295897","https://openalex.org/W4282965153","https://openalex.org/W4283068819","https://openalex.org/W4287781723","https://openalex.org/W4294975451","https://openalex.org/W6637618735","https://openalex.org/W6781493320","https://openalex.org/W6838494425"],"related_works":["https://openalex.org/W2732360296","https://openalex.org/W2118717649","https://openalex.org/W1994871954","https://openalex.org/W2413243053","https://openalex.org/W410723623","https://openalex.org/W2015341305","https://openalex.org/W4297152434","https://openalex.org/W2072858761","https://openalex.org/W2035068594","https://openalex.org/W4225593417"],"abstract_inverted_index":{"Blood":[0],"pressure":[1],"(BP)":[2],"estimation":[3,26,62,87,142,152],"methods":[4,18,27],"using":[5,28,79,103],"photoplethysmogram":[6],"(PPG)":[7],"based":[8],"on":[9],"deep":[10],"learning":[11],"models":[12],"have":[13],"been":[14],"actively":[15],"studied.":[16],"These":[17],"are":[19,39,45,70,119,122],"also":[20],"the":[21,66,106,123,128,140,150,155,169,197,207,227],"basis":[22],"of":[23,165],"non-contact":[24],"BP":[25,61,86,92,96,110,113,117,141,151,192],"a":[29,32,58,80,161],"camera":[30],"or":[31],"Doppler":[33],"radar.":[34],"However,":[35],"most":[36],"previous":[37,170],"studies":[38,171],"under":[40,65,154,196],"data":[41,76,167],"leakage,":[42],"where":[43],"subjects":[44,69],"not":[46],"separated":[47,71],"between":[48,72,226],"training":[49,73,166],"and":[50,74,115,172,212,218,229,236],"test":[51,75],"data.":[52],"In":[53,131],"this":[54],"paper,":[55],"we":[56,133,159],"propose":[57],"method":[59,88,189],"for":[60,127,180,206],"from":[63,98],"PPG":[64,101],"condition":[67],"that":[68,187],"(inter-subject":[77],"paradigm)":[78],"subject-distinguishable":[81],"large":[82],"public":[83],"dataset.":[84],"Our":[85,183],"estimates":[89],"an":[90,99],"8-second":[91,100,129],"waveform":[93],"called":[94],"arterial":[95],"(ABP)":[97],"segment":[102],"U-Net.":[104],"From":[105],"estimated":[107,208,230],"ABP,":[108],"systolic":[109],"(SBP),":[111],"diastolic":[112],"(DBP),":[114],"mean":[116,203],"(MBP)":[118],"calculated,":[120],"which":[121,138],"discrete":[124],"single":[125],"values":[126,231],"ABP.":[130],"addition,":[132],"apply":[134],"domain":[135],"adversarial":[136],"training,":[137],"facilitates":[139],"model":[143,175,178],"to":[144,148],"extract":[145],"subject-invariant":[146],"features":[147],"improve":[149],"accuracy":[153,195],"inter-subject":[156,198],"paradigm.":[157],"Moreover,":[158],"use":[160],"considerably":[162],"larger":[163],"amount":[164],"than":[168],"evaluate":[173],"our":[174,188],"with":[176,193],"smaller":[177],"sizes":[179],"better":[181],"generalizability.":[182],"experimental":[184],"results":[185],"showed":[186],"can":[190],"estimate":[191],"moderate":[194],"paradigm,":[199],"particularly":[200],"MBP.":[201],"The":[202,222],"absolute":[204],"errors":[205],"SBP,":[209],"DBP,":[210],"MBP,":[211],"ABP":[213],"were":[214,232],"15.21,":[215],"7.12,":[216],"8.20,":[217],"10.14":[219],"mmHg,":[220],"respectively.":[221,238],"Pearson's":[223],"correlation":[224],"coefficients":[225],"true":[228],"0.49,":[233],"0.39,":[234],"0.54,":[235],"0.84,":[237]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
