{"id":"https://openalex.org/W4292265988","doi":"https://doi.org/10.1109/jbhi.2022.3199199","title":"Data-Driven Guided Attention for Analysis of Physiological Waveforms With Deep Learning","display_name":"Data-Driven Guided Attention for Analysis of Physiological Waveforms With Deep Learning","publication_year":2022,"publication_date":"2022-08-17","ids":{"openalex":"https://openalex.org/W4292265988","doi":"https://doi.org/10.1109/jbhi.2022.3199199","pmid":"https://pubmed.ncbi.nlm.nih.gov/35976844"},"language":"en","primary_location":{"id":"doi:10.1109/jbhi.2022.3199199","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jbhi.2022.3199199","pdf_url":null,"source":{"id":"https://openalex.org/S2495854775","display_name":"IEEE Journal of Biomedical and Health Informatics","issn_l":"2168-2194","issn":["2168-2194","2168-2208"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Journal of Biomedical and Health Informatics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5081714443","display_name":"Jonathan Martinez","orcid":"https://orcid.org/0000-0003-3947-1574"},"institutions":[{"id":"https://openalex.org/I91045830","display_name":"Texas A&M University","ror":"https://ror.org/01f5ytq51","country_code":"US","type":"education","lineage":["https://openalex.org/I91045830"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jonathan Martinez","raw_affiliation_strings":["Department of Computer Science and Engineering, Texas A&amp;M University, College Station, TX, USA"],"raw_orcid":"https://orcid.org/0000-0003-3947-1574","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Texas A&amp;M University, College Station, TX, USA","institution_ids":["https://openalex.org/I91045830"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073815583","display_name":"Zhale Nowroozilarki","orcid":"https://orcid.org/0000-0002-9608-8312"},"institutions":[{"id":"https://openalex.org/I91045830","display_name":"Texas A&M University","ror":"https://ror.org/01f5ytq51","country_code":"US","type":"education","lineage":["https://openalex.org/I91045830"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhale Nowroozilarki","raw_affiliation_strings":["Department of Computer Science and Engineering, Texas A&amp;M University, College Station, TX, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Texas A&amp;M University, College Station, TX, USA","institution_ids":["https://openalex.org/I91045830"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011769350","display_name":"Roozbeh Jafari","orcid":"https://orcid.org/0000-0002-6358-0458"},"institutions":[{"id":"https://openalex.org/I91045830","display_name":"Texas A&M University","ror":"https://ror.org/01f5ytq51","country_code":"US","type":"education","lineage":["https://openalex.org/I91045830"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Roozbeh Jafari","raw_affiliation_strings":["Department of Biomedical Engineering, Electrical and Computer Engineering, Computer Science and Engineering, Texas A&amp;M University, College Station, TX, USA"],"raw_orcid":"https://orcid.org/0000-0002-6358-0458","affiliations":[{"raw_affiliation_string":"Department of Biomedical Engineering, Electrical and Computer Engineering, Computer Science and Engineering, Texas A&amp;M University, College Station, TX, USA","institution_ids":["https://openalex.org/I91045830"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5040096171","display_name":"Bobak J. Mortazavi","orcid":"https://orcid.org/0000-0002-2655-2095"},"institutions":[{"id":"https://openalex.org/I91045830","display_name":"Texas A&M University","ror":"https://ror.org/01f5ytq51","country_code":"US","type":"education","lineage":["https://openalex.org/I91045830"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bobak J. Mortazavi","raw_affiliation_strings":["Department of Computer Science and Engineering, Texas A&amp;M University, College Station, TX, USA"],"raw_orcid":"https://orcid.org/0000-0002-2655-2095","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Texas A&amp;M University, College Station, TX, USA","institution_ids":["https://openalex.org/I91045830"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I91045830"],"apc_list":null,"apc_paid":null,"fwci":0.4905,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.57615317,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":97},"biblio":{"volume":"26","issue":"11","first_page":"5482","last_page":"5493"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11196","display_name":"Non-Invasive Vital Sign Monitoring","score":0.9997000098228455,"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":0.9997000098228455,"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":0.9987999796867371,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9976999759674072,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/computer-science","display_name":"Computer science","score":0.7374765872955322},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6447160840034485},{"id":"https://openalex.org/keywords/dynamic-time-warping","display_name":"Dynamic time warping","score":0.6101924180984497},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.5933458209037781},{"id":"https://openalex.org/keywords/generalizability-theory","display_name":"Generalizability theory","score":0.560238778591156},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5374013185501099},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.4847126007080078},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.475645512342453},{"id":"https://openalex.org/keywords/waveform","display_name":"Waveform","score":0.47553229331970215},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4426230788230896},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4371776282787323},{"id":"https://openalex.org/keywords/feature-engineering","display_name":"Feature engineering","score":0.4182436466217041},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3654536008834839},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.117757648229599},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11205503344535828}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7374765872955322},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6447160840034485},{"id":"https://openalex.org/C88516994","wikidata":"https://www.wikidata.org/wiki/Q1268863","display_name":"Dynamic time warping","level":2,"score":0.6101924180984497},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.5933458209037781},{"id":"https://openalex.org/C27158222","wikidata":"https://www.wikidata.org/wiki/Q5532422","display_name":"Generalizability theory","level":2,"score":0.560238778591156},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5374013185501099},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.4847126007080078},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.475645512342453},{"id":"https://openalex.org/C197424946","wikidata":"https://www.wikidata.org/wiki/Q1165717","display_name":"Waveform","level":3,"score":0.47553229331970215},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4426230788230896},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4371776282787323},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.4182436466217041},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3654536008834839},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.117757648229599},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11205503344535828},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[{"descriptor_ui":"D000077321","descriptor_name":"Deep Learning","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":true},{"descriptor_ui":"D000077321","descriptor_name":"Deep Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D001288","descriptor_name":"Attention","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D001288","descriptor_name":"Attention","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D001288","descriptor_name":"Attention","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D001794","descriptor_name":"Blood Pressure","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D001794","descriptor_name":"Blood Pressure","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D001794","descriptor_name":"Blood Pressure","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D001795","descriptor_name":"Blood Pressure Determination","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D001795","descriptor_name":"Blood Pressure Determination","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D001795","descriptor_name":"Blood Pressure Determination","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D017097","descriptor_name":"Electric Impedance","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D017097","descriptor_name":"Electric Impedance","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D017097","descriptor_name":"Electric Impedance","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false}],"locations_count":2,"locations":[{"id":"doi:10.1109/jbhi.2022.3199199","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jbhi.2022.3199199","pdf_url":null,"source":{"id":"https://openalex.org/S2495854775","display_name":"IEEE Journal of Biomedical and Health Informatics","issn_l":"2168-2194","issn":["2168-2194","2168-2208"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Journal of Biomedical and Health Informatics","raw_type":"journal-article"},{"id":"pmid:35976844","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/35976844","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":"IEEE journal of biomedical and health informatics","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G795568401","display_name":null,"funder_award_id":"R01 EB028106","funder_id":"https://openalex.org/F4320337363","funder_display_name":"National Institute of Biomedical Imaging and Bioengineering"}],"funders":[{"id":"https://openalex.org/F4320337363","display_name":"National Institute of Biomedical Imaging and Bioengineering","ror":"https://ror.org/00372qc85"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":53,"referenced_works":["https://openalex.org/W1989977532","https://openalex.org/W1992145371","https://openalex.org/W2014822627","https://openalex.org/W2021674224","https://openalex.org/W2082281669","https://openalex.org/W2130003848","https://openalex.org/W2142738495","https://openalex.org/W2144994235","https://openalex.org/W2162193833","https://openalex.org/W2329402546","https://openalex.org/W2396881363","https://openalex.org/W2490300818","https://openalex.org/W2560128284","https://openalex.org/W2608498389","https://openalex.org/W2757251151","https://openalex.org/W2768962095","https://openalex.org/W2780316996","https://openalex.org/W2784502443","https://openalex.org/W2785373760","https://openalex.org/W2890266312","https://openalex.org/W2894956427","https://openalex.org/W2911397453","https://openalex.org/W2916816078","https://openalex.org/W2952383525","https://openalex.org/W2955961229","https://openalex.org/W2963532813","https://openalex.org/W2963606198","https://openalex.org/W2963712527","https://openalex.org/W2965435808","https://openalex.org/W2965846681","https://openalex.org/W2979416271","https://openalex.org/W2980997397","https://openalex.org/W3004151245","https://openalex.org/W3015201783","https://openalex.org/W3016504312","https://openalex.org/W3021152857","https://openalex.org/W3030350170","https://openalex.org/W3045199277","https://openalex.org/W3087316027","https://openalex.org/W3090602117","https://openalex.org/W3101824250","https://openalex.org/W3117498368","https://openalex.org/W3121369665","https://openalex.org/W3157525829","https://openalex.org/W3164139332","https://openalex.org/W3181273692","https://openalex.org/W3196743513","https://openalex.org/W3216552097","https://openalex.org/W4207054915","https://openalex.org/W4285113895","https://openalex.org/W6712503630","https://openalex.org/W6781493320","https://openalex.org/W6794206771"],"related_works":["https://openalex.org/W4380075502","https://openalex.org/W4223943233","https://openalex.org/W4312200629","https://openalex.org/W4360585206","https://openalex.org/W4364306694","https://openalex.org/W4380086463","https://openalex.org/W4225161397","https://openalex.org/W2968586400","https://openalex.org/W2942650110","https://openalex.org/W4281986673"],"abstract_inverted_index":{"Estimating":[0],"physiological":[1],"parameters":[2],"-":[3,9],"such":[4],"as":[5],"blood":[6],"pressure":[7],"(BP)":[8],"from":[10,142],"raw":[11],"sensor":[12],"data":[13,45,172],"captured":[14,71],"by":[15,27,63,194,221],"noninvasive,":[16],"wearable":[17],"devices":[18],"rely":[19],"on":[20],"either":[21],"burdensome":[22],"manual":[23],"feature":[24,140],"extraction":[25],"designed":[26],"domain":[28],"experts":[29],"to":[30,55,59,96,106,113,136,151],"identify":[31,108],"key":[32,122],"waveform":[33,82,167],"characteristics":[34],"and":[35,67,85,173,217],"phases,":[36],"or":[37],"deep":[38],"learning":[39],"(DL)":[40],"models":[41,58,103,193],"that":[42,187],"require":[43],"extensive":[44],"collection.":[46],"We":[47],"propose":[48],"the":[49,64,70,119,125,132,138,165],"Data-Driven":[50],"Guided":[51],"Attention":[52],"(DDGA)":[53],"framework":[54],"optimize":[56],"DL":[57,102,135],"learn":[60],"features":[61],"supported":[62],"underlying":[65],"physiology":[66],"physics":[68],"of":[69,118,124,210,230],"waveforms,":[72],"with":[73,161,169,179],"minimal":[74],"expert":[75],"annotation.":[76],"With":[77],"only":[78],"a":[79,152,175,214],"single":[80],"template":[81],"cardiac":[83,126],"cycle":[84],"its":[86],"labelled":[87],"fiducial":[88],"points,":[89],"we":[90,129,147],"leverage":[91],"dynamic":[92],"time":[93],"warping":[94],"(DTW)":[95],"annotate":[97],"all":[98],"other":[99],"training":[100,171,183,215],"samples.":[101],"are":[104],"trained":[105],"first":[107],"them":[109,115],"before":[110],"estimating":[111],"BP":[112,153,191,231],"inform":[114],"which":[116],"regions":[117],"input":[120],"represent":[121],"phases":[123],"cycle,":[127],"yet":[128],"still":[130],"grant":[131],"flexibility":[133],"for":[134,156],"determine":[137],"optimal":[139],"set":[141,216],"them.":[143],"In":[144],"this":[145],"study,":[146],"evaluate":[148],"DDGA's":[149],"improvements":[150],"estimation":[154,192,229],"task":[155],"three":[157],"prominent":[158],"DL-based":[159],"architectures":[160],"two":[162],"datasets:":[163],"1)":[164],"MIMIC-III":[166],"dataset":[168,178],"ample":[170],"2)":[174],"bio-impedance":[176],"(Bio-Z)":[177],"less":[180],"than":[181],"abundant":[182],"data.":[184],"Experiments":[185],"show":[186],"DDGA":[188],"improves":[189,218],"personalized":[190],"an":[195,207,222],"average":[196,223],"8.14%":[197],"in":[198,213,225,237],"root":[199],"mean":[200],"square":[201],"error":[202],"(RMSE)":[203],"when":[204,227],"there":[205],"is":[206],"imbalanced":[208],"distribution":[209],"target":[211],"values":[212],"model":[219],"generalizability":[220],"4.92%":[224],"RMSE":[226],"testing":[228],"value":[232],"ranges":[233],"not":[234],"previously":[235],"seen":[236],"training.":[238]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":2}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}
