{"id":"https://openalex.org/W4417296689","doi":"https://doi.org/10.1016/j.cmpb.2026.109479","title":"PMB-NN: Physiology-centered hybrid AI for personalized hemodynamic monitoring from photoplethysmography","display_name":"PMB-NN: Physiology-centered hybrid AI for personalized hemodynamic monitoring from photoplethysmography","publication_year":2026,"publication_date":"2026-06-04","ids":{"openalex":"https://openalex.org/W4417296689","doi":"https://doi.org/10.1016/j.cmpb.2026.109479","pmid":"https://pubmed.ncbi.nlm.nih.gov/42248129"},"language":"en","primary_location":{"id":"doi:10.1016/j.cmpb.2026.109479","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.cmpb.2026.109479","pdf_url":null,"source":{"id":"https://openalex.org/S138959710","display_name":"Computer Methods and Programs in Biomedicine","issn_l":"0169-2607","issn":["0169-2607","1872-7565"],"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":"Computer Methods and Programs in Biomedicine","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref","datacite","pubmed"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1016/j.cmpb.2026.109479","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100677020","display_name":"Yaowen Zhang","orcid":"https://orcid.org/0000-0003-4218-5771"},"institutions":[{"id":"https://openalex.org/I94624287","display_name":"University of Twente","ror":"https://ror.org/006hf6230","country_code":"NL","type":"education","lineage":["https://openalex.org/I94624287"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Yaowen Zhang","raw_affiliation_strings":["Department of Biomedical Signals and Systems, University of Twente, Enschede, 7522NB, The Netherlands. Electronic address: y.zhang-12@utwente.nl"],"raw_orcid":"https://orcid.org/0009-0008-2450-7881","affiliations":[{"raw_affiliation_string":"Department of Biomedical Signals and Systems, University of Twente, Enschede, 7522NB, The Netherlands. Electronic address: y.zhang-12@utwente.nl","institution_ids":["https://openalex.org/I94624287"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058787139","display_name":"Libera Fresiello","orcid":"https://orcid.org/0000-0003-0972-8340"},"institutions":[{"id":"https://openalex.org/I94624287","display_name":"University of Twente","ror":"https://ror.org/006hf6230","country_code":"NL","type":"education","lineage":["https://openalex.org/I94624287"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Libera Fresiello","raw_affiliation_strings":["Department of Cardiovascular and Respiratory Physiology, University of Twente, Enschede, 7522NB, The Netherlands. Electronic address: l.fresiello@utwente.nl"],"raw_orcid":"https://orcid.org/0000-0003-0972-8340","affiliations":[{"raw_affiliation_string":"Department of Cardiovascular and Respiratory Physiology, University of Twente, Enschede, 7522NB, The Netherlands. Electronic address: l.fresiello@utwente.nl","institution_ids":["https://openalex.org/I94624287"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080611863","display_name":"Petrus H. Veltink","orcid":"https://orcid.org/0000-0002-1796-9999"},"institutions":[{"id":"https://openalex.org/I94624287","display_name":"University of Twente","ror":"https://ror.org/006hf6230","country_code":"NL","type":"education","lineage":["https://openalex.org/I94624287"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Peter H. Veltink","raw_affiliation_strings":["Department of Biomedical Signals and Systems, University of Twente, Enschede, 7522NB, The Netherlands. Electronic address: p.h.veltink@utwente.nl"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Biomedical Signals and Systems, University of Twente, Enschede, 7522NB, The Netherlands. Electronic address: p.h.veltink@utwente.nl","institution_ids":["https://openalex.org/I94624287"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075055397","display_name":"Dirk W. Donker","orcid":"https://orcid.org/0000-0001-6496-2768"},"institutions":[{"id":"https://openalex.org/I94624287","display_name":"University of Twente","ror":"https://ror.org/006hf6230","country_code":"NL","type":"education","lineage":["https://openalex.org/I94624287"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Dirk W. Donker","raw_affiliation_strings":["Department of Cardiovascular and Respiratory Physiology, University of Twente, Enschede, 7522NB, The Netherlands; Department of Intensive Care, University Medical Center Utrecht, Utrecht, 3584CX, The Netherlands. Electronic address: d.w.donker@utwente.nl"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Cardiovascular and Respiratory Physiology, University of Twente, Enschede, 7522NB, The Netherlands; Department of Intensive Care, University Medical Center Utrecht, Utrecht, 3584CX, The Netherlands. Electronic address: d.w.donker@utwente.nl","institution_ids":["https://openalex.org/I94624287"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100346975","display_name":"Ying Wang","orcid":"https://orcid.org/0000-0001-6470-0020"},"institutions":[{"id":"https://openalex.org/I94624287","display_name":"University of Twente","ror":"https://ror.org/006hf6230","country_code":"NL","type":"education","lineage":["https://openalex.org/I94624287"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Ying Wang","raw_affiliation_strings":["Department of Biomedical Signals and Systems, University of Twente, Enschede, 7522NB, The Netherlands. Electronic address: imwywk@gmail.com"],"raw_orcid":"https://orcid.org/0000-0003-4385-9117","affiliations":[{"raw_affiliation_string":"Department of Biomedical Signals and Systems, University of Twente, Enschede, 7522NB, The Netherlands. Electronic address: imwywk@gmail.com","institution_ids":["https://openalex.org/I94624287"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I94624287"],"apc_list":{"value":3350,"currency":"USD","value_usd":3350},"apc_paid":{"value":3350,"currency":"USD","value_usd":3350},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.00736184,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"285","issue":null,"first_page":"109479","last_page":"109479"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11196","display_name":"Non-Invasive Vital Sign Monitoring","score":0.9876000285148621,"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":0.9876000285148621,"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/T10745","display_name":"Heart Rate Variability and Autonomic Control","score":0.002899999963119626,"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"}},{"id":"https://openalex.org/T10977","display_name":"Optical Imaging and Spectroscopy Techniques","score":0.0026000000070780516,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.9146000146865845},{"id":"https://openalex.org/keywords/photoplethysmogram","display_name":"Photoplethysmogram","score":0.8561999797821045},{"id":"https://openalex.org/keywords/hemodynamics","display_name":"Hemodynamics","score":0.6517999768257141},{"id":"https://openalex.org/keywords/blood-pressure","display_name":"Blood pressure","score":0.5408999919891357},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4894999861717224},{"id":"https://openalex.org/keywords/compliance","display_name":"Compliance (psychology)","score":0.47929999232292175},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.41200000047683716}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.9146000146865845},{"id":"https://openalex.org/C116390426","wikidata":"https://www.wikidata.org/wiki/Q7187885","display_name":"Photoplethysmogram","level":3,"score":0.8561999797821045},{"id":"https://openalex.org/C178853913","wikidata":"https://www.wikidata.org/wiki/Q1642137","display_name":"Hemodynamics","level":2,"score":0.6517999768257141},{"id":"https://openalex.org/C84393581","wikidata":"https://www.wikidata.org/wiki/Q82642","display_name":"Blood pressure","level":2,"score":0.5408999919891357},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.513700008392334},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4894999861717224},{"id":"https://openalex.org/C2781460075","wikidata":"https://www.wikidata.org/wiki/Q1399332","display_name":"Compliance (psychology)","level":2,"score":0.47929999232292175},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4359999895095825},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.41200000047683716},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3993000090122223},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.397599995136261},{"id":"https://openalex.org/C164705383","wikidata":"https://www.wikidata.org/wiki/Q10379","display_name":"Cardiology","level":1,"score":0.3472999930381775},{"id":"https://openalex.org/C58245268","wikidata":"https://www.wikidata.org/wiki/Q456026","display_name":"Vascular resistance","level":3,"score":0.3418000042438507},{"id":"https://openalex.org/C139940330","wikidata":"https://www.wikidata.org/wiki/Q596579","display_name":"Cardiac output","level":3,"score":0.3391999900341034},{"id":"https://openalex.org/C57900726","wikidata":"https://www.wikidata.org/wiki/Q492905","display_name":"Diastole","level":3,"score":0.33660000562667847},{"id":"https://openalex.org/C2986265897","wikidata":"https://www.wikidata.org/wiki/Q23852","display_name":"Human physiology","level":2,"score":0.26570001244544983},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.26489999890327454},{"id":"https://openalex.org/C17140001","wikidata":"https://www.wikidata.org/wiki/Q1759415","display_name":"Pulse pressure","level":3,"score":0.2639999985694885},{"id":"https://openalex.org/C48277249","wikidata":"https://www.wikidata.org/wiki/Q1406926","display_name":"Preload","level":3,"score":0.2621000111103058}],"mesh":[{"descriptor_ui":"D000074743","descriptor_name":"Hemodynamic Monitoring","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000077321","descriptor_name":"Deep Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000328","descriptor_name":"Adult","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D001185","descriptor_name":"Artificial Intelligence","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D001794","descriptor_name":"Blood Pressure","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D005260","descriptor_name":"Female","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006439","descriptor_name":"Hemodynamics","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008297","descriptor_name":"Male","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D017156","descriptor_name":"Photoplethysmography","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D055815","descriptor_name":"Young Adult","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false}],"locations_count":6,"locations":[{"id":"doi:10.1016/j.cmpb.2026.109479","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.cmpb.2026.109479","pdf_url":null,"source":{"id":"https://openalex.org/S138959710","display_name":"Computer Methods and Programs in Biomedicine","issn_l":"0169-2607","issn":["0169-2607","1872-7565"],"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":"Computer Methods and Programs in Biomedicine","raw_type":"journal-article"},{"id":"pmid:42248129","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/42248129","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computer methods and programs in biomedicine","raw_type":"Journal Article"},{"id":"pmh:oai:ris.utwente.nl:openaire/5a0e47f6-8736-4fc5-9e07-d6475fd280fb","is_oa":true,"landing_page_url":"https://research.utwente.nl/en/publications/5a0e47f6-8736-4fc5-9e07-d6475fd280fb","pdf_url":null,"source":{"id":"https://openalex.org/S4406922991","display_name":"University of Twente Research Information","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":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Zhang, Y, Fresiello, L, Veltink, P H, Donker, D W & Wang, Y 2026, 'PMB-NN : Physiology-centered hybrid AI for personalized hemodynamic monitoring from photoplethysmography', Computer methods and programs in biomedicine, vol. 285, 109479. https://doi.org/10.1016/j.cmpb.2026.109479","raw_type":"info:eu-repo/semantics/article"},{"id":"pmh:oai:arXiv.org:2512.10745","is_oa":true,"landing_page_url":"https://arxiv.org/abs/2512.10745","pdf_url":"https://arxiv.org/pdf/2512.10745","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:ris.utwente.nl:openaire/4d6bc9ae-4ef8-45ab-8830-5b787db5db14","is_oa":false,"landing_page_url":"https://research.utwente.nl/en/publications/4d6bc9ae-4ef8-45ab-8830-5b787db5db14","pdf_url":null,"source":{"id":"https://openalex.org/S4406922991","display_name":"University of Twente Research Information","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":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Zhang, Y, Fresiello, L, Veltink, P H, Donker, D W & Wang, Y 2025 'PMB-NN : Physiology-Centred Hybrid AI for Personalized Hemodynamic Monitoring from Photoplethysmography' ArXiv.org. https://doi.org/10.48550/arXiv.2512.10745","raw_type":"info:eu-repo/semantics/preprint"},{"id":"doi:10.48550/arxiv.2512.10745","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2512.10745","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.1016/j.cmpb.2026.109479","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.cmpb.2026.109479","pdf_url":null,"source":{"id":"https://openalex.org/S138959710","display_name":"Computer Methods and Programs in Biomedicine","issn_l":"0169-2607","issn":["0169-2607","1872-7565"],"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":"Computer Methods and Programs in Biomedicine","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320322725","display_name":"China Scholarship Council","ror":"https://ror.org/04atp4p48"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"BACKGROUND":[0],"AND":[1],"OBJECTIVE:":[2],"Continuous":[3],"monitoring":[4],"of":[5,144,224,231,311],"blood":[6],"pressure":[7],"(BP)":[8],"and":[9,17,28,81,123,132,152,168,200,202,213,221,237,298,306,314,328,373],"hemodynamic":[10,64,393,421],"parameters":[11],"such":[12],"as":[13,84,100,115,170,172,377,409],"peripheral":[14],"resistance":[15],"(R)":[16],"arterial":[18,63],"compliance":[19],"(C)":[20],"are":[21],"critical":[22],"for":[23,42,121,186,381,419],"early":[24],"vascular":[25],"dysfunction":[26],"detection":[27],"therapy":[29],"optimization.":[30],"While":[31,383],"photoplethysmography":[32],"(PPG)":[33],"wearables":[34],"has":[35],"gained":[36],"widespread":[37],"popularity,":[38],"existing":[39],"data-driven":[40,315,417],"methods":[41],"BP":[43,212,264],"estimation":[44,184,235,338],"lack":[45],"physiological":[46,75,204,292,312,349,398],"interpretability.":[47],"METHODS:":[48],"We":[49],"advanced":[50],"our":[51],"previously":[52],"proposed":[53],"physiology-centered":[54],"hybrid":[55,303,355],"AI":[56,356],"method-the":[57],"Physiological":[58],"Model-Based":[59],"Neural":[60],"Network":[61],"(PMB-NN)-in":[62],"monitoring,":[65],"that":[66,301,346],"unifies":[67,305],"deep":[68,162],"learning":[69,163],"with":[70],"a":[71,93,116,141,180,378,410],"2-element":[72],"Windkessel":[73],"based":[74],"model":[76,89,128,242],"(PM)":[77],"parameterized":[78],"by":[79,193],"R":[80,122,199,322],"C":[82,124,329],"acting":[83],"physics":[85],"constraints.":[86],"The":[87,127,135],"PMB-NN":[88,136,261,288,319,408],"was":[90,105,137],"trained":[91],"in":[92],"subject-specific":[94],"manner":[95],"using":[96],"PPG-derived":[97],"timing":[98,215],"features":[99],"inputs,":[101],"while":[102,240,395],"demographic":[103],"information":[104],"used":[106],"to":[107,118,155,197,248,271,341,353,365,415],"infer":[108,198],"cardiac":[109],"output":[110],"(Q)":[111],"which":[112],"is":[113],"integrated":[114],"prerequisite":[117],"solve":[119],"PM":[120],"during":[125,334],"training.":[126],"outputs":[129],"personalized":[130],"systolic":[131,263],"diastolic":[133,276],"BP.":[134],"initially":[138],"validated":[139],"on":[140],"primary":[142],"cohort":[143,255],"10":[145],"healthy":[146],"young":[147],"adults":[148],"performing":[149],"multi-day":[150],"static":[151],"cycling":[153],"activities":[154],"assess":[156],"day-to-day":[157],"robustness,":[158],"benchmarked":[159],"against":[160],"established":[161],"(DL)":[164],"models":[165],"(FCNN,":[166],"CNN-LSTM,":[167],"Transformer)":[169],"well":[171],"the":[173,194,208,219,225,229,302,308,342,347,354,362,401],"standalone":[174,343],"PM.":[175],"This":[176],"core":[177],"evaluation":[178],"followed":[179],"tripartite":[181],"framework:":[182],"(i)":[183],"accuracy":[185,265],"BP;":[187],"(ii)":[188],"physiologically":[189,412],"constrained":[190],"interpretability,":[191],"reflected":[192],"model's":[195],"ability":[196],"C;":[201],"(iii)":[203],"plausibility,":[205],"assessed":[206],"via":[207],"correlation":[209],"between":[210],"estimated":[211],"input":[214],"features.":[216],"To":[217],"explore":[218],"mechanistic":[220],"operational":[222],"boundaries":[223],"framework,":[226],"we":[227],"quantified":[228],"impacts":[230],"motion":[232,367],"artifacts,":[233],"Q":[234],"errors,":[236],"calibration":[238,376],"efficiency,":[239],"assessing":[241],"transferability":[243],"from":[244],"exercise-driven":[245],"steady":[246],"states":[247],"distinct":[249],"autonomic":[250,388],"challenges":[251],"across":[252,400],"an":[253],"age-diverse":[254],"(n=37,":[256],"18-88":[257],"years":[258],"old).":[259],"RESULTS:":[260],"achieved":[262],"(median":[266,278,323,330],"MAE:":[267,279,324,331],"7.2":[268],"mmHg)":[269,281],"comparable":[270],"DL":[272,284,296],"benchmarks,":[273],"despite":[274],"yielding":[275],"performance":[277],"3.9":[280],"lower":[282],"than":[283,294],"models.":[285],"Crucially,":[286],"however,":[287],"exhibited":[289],"substantially":[290],"higher":[291],"plausibility":[293,399],"both":[295],"baselines":[297],"PM,":[299,344],"suggesting":[300],"architecture":[304],"enhances":[307],"respective":[309],"merits":[310],"principles":[313],"techniques.":[316],"Beyond":[317],"BP,":[318],"also":[320],"identified":[321,374],"0.13":[325],"mmHg":[326],"s/ml)":[327],"0.19":[332],"ml/mmHg)":[333],"training,":[335],"achieving":[336],"parameter":[337],"fidelity":[339],"similar":[340],"demonstrating":[345],"embedded":[348],"constraints":[350],"confer":[351],"interpretability":[352],"framework.":[357],"Furthermore,":[358],"extended":[359],"evaluations":[360],"confirmed":[361],"framework's":[363],"resilience":[364],"moderate":[366],"artifacts":[368],"(SNR":[369],"=":[370],"15":[371],"dB)":[372],"5-min":[375],"pragmatic":[379],"threshold":[380],"personalization.":[382],"numerical":[384],"errors":[385],"increased":[386],"under":[387],"challenges,":[389],"it":[390],"captured":[391],"age-related":[392],"shifts":[394],"maintaining":[396],"consistent":[397],"adult":[402],"lifespan.":[403],"CONCLUSIONS:":[404],"These":[405],"results":[406],"position":[407],"balanced,":[411],"grounded":[413],"alternative":[414],"purely":[416],"approaches":[418],"daily":[420],"monitoring.":[422]},"counts_by_year":[],"updated_date":"2026-08-21T09:56:20.448147","created_date":"2025-12-13T00:00:00"}