{"id":"https://openalex.org/W7131398595","doi":"https://doi.org/10.1109/access.2026.3667900","title":"Domain Transfer of Generative AI for Estimating Electrophysiological Changes Through Hemodynamic Variations in Optical Blood Flow: Potential and Performance Evaluation Methodology","display_name":"Domain Transfer of Generative AI for Estimating Electrophysiological Changes Through Hemodynamic Variations in Optical Blood Flow: Potential and Performance Evaluation Methodology","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7131398595","doi":"https://doi.org/10.1109/access.2026.3667900"},"language":null,"primary_location":{"id":"doi:10.1109/access.2026.3667900","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3667900","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2026.3667900","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100825048","display_name":"Sanghoon Choi","orcid":null},"institutions":[{"id":"https://openalex.org/I2802194831","display_name":"Samsung Medical Center","ror":"https://ror.org/05a15z872","country_code":"KR","type":"healthcare","lineage":["https://openalex.org/I2250650973","https://openalex.org/I2802194831"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Sanghoon Choi","raw_affiliation_strings":["Digital Therapeutics Research Center, Smart Healthcare Research Institute, Samsung Medical Center, Seoul, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Digital Therapeutics Research Center, Smart Healthcare Research Institute, Samsung Medical Center, Seoul, South Korea","institution_ids":["https://openalex.org/I2802194831"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126849082","display_name":"SungJun Hong","orcid":null},"institutions":[{"id":"https://openalex.org/I2802194831","display_name":"Samsung Medical Center","ror":"https://ror.org/05a15z872","country_code":"KR","type":"healthcare","lineage":["https://openalex.org/I2250650973","https://openalex.org/I2802194831"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Sungjun Hong","raw_affiliation_strings":["Biomedical Engineering Research Center, Smart Healthcare Research Institute, Samsung Medical Center, Seoul, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Biomedical Engineering Research Center, Smart Healthcare Research Institute, Samsung Medical Center, Seoul, South Korea","institution_ids":["https://openalex.org/I2802194831"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103975891","display_name":"Hyo-Chang Seo","orcid":null},"institutions":[{"id":"https://openalex.org/I2802194831","display_name":"Samsung Medical Center","ror":"https://ror.org/05a15z872","country_code":"KR","type":"healthcare","lineage":["https://openalex.org/I2250650973","https://openalex.org/I2802194831"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Hyo-Chang Seo","raw_affiliation_strings":["Digital Therapeutics Research Center, Smart Healthcare Research Institute, Samsung Medical Center, Seoul, South Korea"],"raw_orcid":"https://orcid.org/0000-0002-8046-6374","affiliations":[{"raw_affiliation_string":"Digital Therapeutics Research Center, Smart Healthcare Research Institute, Samsung Medical Center, Seoul, South Korea","institution_ids":["https://openalex.org/I2802194831"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I2802194831"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.21878488,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"14","issue":null,"first_page":"31343","last_page":"31357"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.18459999561309814,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.18459999561309814,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11438","display_name":"Retinal Imaging and Analysis","score":0.09030000120401382,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"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/T12611","display_name":"Neural Networks and Reservoir Computing","score":0.052400000393390656,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/discriminator","display_name":"Discriminator","score":0.8841000199317932},{"id":"https://openalex.org/keywords/photoplethysmogram","display_name":"Photoplethysmogram","score":0.5900999903678894},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5253000259399414},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4345000088214874},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4041999876499176},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.38359999656677246},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.3747999966144562},{"id":"https://openalex.org/keywords/cardiac-electrophysiology","display_name":"Cardiac electrophysiology","score":0.37220001220703125},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.37049999833106995}],"concepts":[{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.8841000199317932},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7491999864578247},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6101999878883362},{"id":"https://openalex.org/C116390426","wikidata":"https://www.wikidata.org/wiki/Q7187885","display_name":"Photoplethysmogram","level":3,"score":0.5900999903678894},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5253000259399414},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4345000088214874},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4041999876499176},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.38359999656677246},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.3747999966144562},{"id":"https://openalex.org/C61381695","wikidata":"https://www.wikidata.org/wiki/Q3302130","display_name":"Cardiac electrophysiology","level":3,"score":0.37220001220703125},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.37049999833106995},{"id":"https://openalex.org/C19118579","wikidata":"https://www.wikidata.org/wiki/Q786423","display_name":"Frequency domain","level":2,"score":0.3691999912261963},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3628999888896942},{"id":"https://openalex.org/C2780040984","wikidata":"https://www.wikidata.org/wiki/Q79785","display_name":"Electrocardiography","level":2,"score":0.36169999837875366},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.349700003862381},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.34540000557899475},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3325999975204468},{"id":"https://openalex.org/C81299745","wikidata":"https://www.wikidata.org/wiki/Q334269","display_name":"Transfer function","level":2,"score":0.32269999384880066},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3215000033378601},{"id":"https://openalex.org/C103824480","wikidata":"https://www.wikidata.org/wiki/Q185889","display_name":"Time domain","level":2,"score":0.3057999908924103},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.30309998989105225},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.30090001225471497},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.2987000048160553},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.29429998993873596},{"id":"https://openalex.org/C2988773926","wikidata":"https://www.wikidata.org/wiki/Q25104379","display_name":"Generative adversarial network","level":3,"score":0.29159998893737793},{"id":"https://openalex.org/C58471807","wikidata":"https://www.wikidata.org/wiki/Q327120","display_name":"Receiver operating characteristic","level":2,"score":0.2903999984264374},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.28619998693466187},{"id":"https://openalex.org/C175079658","wikidata":"https://www.wikidata.org/wiki/Q7312165","display_name":"Remote patient monitoring","level":2,"score":0.2678999900817871},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.26339998841285706}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/access.2026.3667900","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3667900","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1109/access.2026.3667900","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3667900","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"},{"id":"https://openalex.org/F4320335489","display_name":"Institute for Information and Communications Technology Promotion","ror":"https://ror.org/01g0hqq23"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Continuous":[0],"cardiac":[1,221],"monitoring":[2,222],"is":[3,24],"essential":[4],"for":[5,209],"the":[6,79,82,136,177,201,216],"early":[7],"detection":[8,227],"and":[9,76,87,105,124,129,142,144,150,169,186,223,228],"management":[10],"of":[11,84,161,167,174,207,218],"cardiovascular":[12],"diseases":[13],"(CVDs).":[14],"Although":[15],"electrocardiography":[16],"(ECG)":[17],"provides":[18],"detailed":[19],"electrophysiological":[20],"information,":[21],"its":[22],"accessibility":[23],"limited.":[25],"In":[26],"contrast,":[27],"photoplethysmography":[28],"(PPG)":[29],"integrates":[30],"easily":[31],"into":[32],"wearable":[33],"devices,":[34],"although":[35],"it":[36],"lacks":[37],"ECG-level":[38],"diagnostic":[39,195],"precision.":[40],"To":[41],"address":[42],"this":[43],"limitation,":[44],"we":[45],"propose":[46],"a":[47,65,99,106,155],"deep":[48],"learning-based":[49],"domain":[50,125],"transfer":[51],"framework":[52,97],"that":[53],"reconstructs":[54],"ECG":[55,132,189],"signals":[56,190],"from":[57,192],"PPG.":[58],"This":[59],"method":[60],"utilizes":[61],"self-supervised":[62],"learning":[63,75],"alongside":[64],"transformer-based":[66],"conditional":[67],"generative":[68],"adversarial":[69],"network":[70],"(Trans-cGAN).":[71],"By":[72],"combining":[73],"contrastive":[74],"masked":[77],"modeling,":[78],"approach":[80],"improves":[81],"capture":[83],"temporal":[85,122],"dependencies":[86],"inter-modal":[88],"relationships,":[89],"minimizing":[90],"reliance":[91],"on":[92,138,176],"paired":[93],"datasets.":[94],"The":[95,152],"Trans-cGAN":[96],"incorporates":[98],"U-Net-based":[100],"generator":[101],"with":[102],"transformer":[103],"blocks":[104],"pre-trained":[107],"encoder-based":[108],"discriminator":[109],"to":[110],"improve":[111],"physiological":[112],"coherence.":[113],"A":[114],"comprehensive":[115],"loss":[116],"function\u2014comprising":[117],"adversarial,":[118],"reconstruction,":[119],"feature":[120],"matching,":[121],"coherence,":[123],"consistency":[126],"losses\u2014facilitates":[127],"realistic":[128],"clinically":[130],"relevant":[131],"synthesis.":[133],"We":[134],"evaluated":[135],"model":[137,153],"large-scale":[139],"datasets":[140,147],"(VitalDB":[141],"MIMIC-III)":[143],"external":[145],"validation":[146],"(MIMIC-PerformAfib,":[148],"CapnoBase,":[149],"PPG-DaLiA).":[151],"yielded":[154],"root":[156],"mean":[157,163],"square":[158],"error":[159,165],"(RMSE)":[160],"0.1315,":[162],"absolute":[164],"(MAE)":[166],"0.0862,":[168],"Fr\u00e9chet":[170],"distance":[171],"(FD)":[172],"score":[173],"0.3678":[175],"MIMIC-III":[178],"dataset,":[179],"outperforming":[180],"existing":[181],"methods":[182],"such":[183],"as":[184],"CardioGAN":[185],"CLEP-GAN.":[187],"Furthermore,":[188],"generated":[191],"PPG":[193],"retained":[194],"features,":[196],"achieving":[197],"an":[198],"area":[199],"under":[200],"receiver":[202],"operating":[203],"characteristic":[204],"curve":[205],"(AUROC)":[206],"0.96":[208],"atrial":[210],"fibrillation":[211],"detection.":[212],"These":[213],"results":[214],"highlight":[215],"feasibility":[217],"real-time,":[219],"wearable-based":[220],"enhanced":[224],"noninvasive":[225],"CVD":[226],"management.":[229]},"counts_by_year":[],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2026-02-26T00:00:00"}
