{"id":"https://openalex.org/W2964606899","doi":"https://doi.org/10.1109/access.2019.2933498","title":"Blood Pressure Estimation From Beat-by-Beat Time-Domain Features of Oscillometric Waveforms Using Deep-Neural-Network Classification Models","display_name":"Blood Pressure Estimation From Beat-by-Beat Time-Domain Features of Oscillometric Waveforms Using Deep-Neural-Network Classification Models","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2964606899","doi":"https://doi.org/10.1109/access.2019.2933498","mag":"2964606899"},"language":"en","primary_location":{"id":"doi:10.1109/access.2019.2933498","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2933498","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08789404.pdf","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://ieeexplore.ieee.org/ielx7/6287639/8600701/08789404.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5038653363","display_name":"Ahmadreza Argha","orcid":"https://orcid.org/0000-0002-8276-9774"},"institutions":[{"id":"https://openalex.org/I31746571","display_name":"UNSW Sydney","ror":"https://ror.org/03r8z3t63","country_code":"AU","type":"education","lineage":["https://openalex.org/I31746571"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Ahmadreza Argha","raw_affiliation_strings":["School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW, Australia"],"raw_orcid":"https://orcid.org/0000-0002-8276-9774","affiliations":[{"raw_affiliation_string":"School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW, Australia","institution_ids":["https://openalex.org/I31746571"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Ji Wu","orcid":"https://orcid.org/0000-0003-2641-7739"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ji Wu","raw_affiliation_strings":["Shenzhen ET Medical Technology Company Ltd., Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0003-2641-7739","affiliations":[{"raw_affiliation_string":"Shenzhen ET Medical Technology Company Ltd., Shenzhen, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055012056","display_name":"Steven W. Su","orcid":"https://orcid.org/0000-0002-5720-8852"},"institutions":[{"id":"https://openalex.org/I114017466","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041","country_code":"AU","type":"education","lineage":["https://openalex.org/I114017466"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Steven W. Su","raw_affiliation_strings":["Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, Australia","institution_ids":["https://openalex.org/I114017466"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5007973281","display_name":"Branko G. Celler","orcid":"https://orcid.org/0000-0003-3790-2895"},"institutions":[{"id":"https://openalex.org/I31746571","display_name":"UNSW Sydney","ror":"https://ror.org/03r8z3t63","country_code":"AU","type":"education","lineage":["https://openalex.org/I31746571"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Branko G. Celler","raw_affiliation_strings":["School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW, Australia","institution_ids":["https://openalex.org/I31746571"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":1.4519,"has_fulltext":true,"cited_by_count":32,"citation_normalized_percentile":{"value":0.80439117,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"7","issue":null,"first_page":"113427","last_page":"113439"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11196","display_name":"Non-Invasive Vital Sign Monitoring","score":0.9993000030517578,"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":0.9993000030517578,"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":0.9990000128746033,"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/T11021","display_name":"ECG Monitoring and Analysis","score":0.9988999962806702,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.689021646976471},{"id":"https://openalex.org/keywords/beat","display_name":"Beat (acoustics)","score":0.6392894983291626},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6309522986412048},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6225810050964355},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5833514928817749},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5825478434562683},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5770436525344849},{"id":"https://openalex.org/keywords/deep-belief-network","display_name":"Deep belief network","score":0.4802394509315491},{"id":"https://openalex.org/keywords/time-domain","display_name":"Time domain","score":0.47319671511650085},{"id":"https://openalex.org/keywords/blood-pressure","display_name":"Blood pressure","score":0.4421460032463074},{"id":"https://openalex.org/keywords/frequency-domain","display_name":"Frequency domain","score":0.4178091287612915},{"id":"https://openalex.org/keywords/waveform","display_name":"Waveform","score":0.414132297039032},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.34399545192718506},{"id":"https://openalex.org/keywords/acoustics","display_name":"Acoustics","score":0.11316150426864624},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.10305783152580261},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.09587591886520386},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.08342674374580383},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.08313089609146118},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.07765302062034607}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.689021646976471},{"id":"https://openalex.org/C189809214","wikidata":"https://www.wikidata.org/wiki/Q829522","display_name":"Beat (acoustics)","level":2,"score":0.6392894983291626},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6309522986412048},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6225810050964355},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5833514928817749},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5825478434562683},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5770436525344849},{"id":"https://openalex.org/C97385483","wikidata":"https://www.wikidata.org/wiki/Q16954980","display_name":"Deep belief network","level":3,"score":0.4802394509315491},{"id":"https://openalex.org/C103824480","wikidata":"https://www.wikidata.org/wiki/Q185889","display_name":"Time domain","level":2,"score":0.47319671511650085},{"id":"https://openalex.org/C84393581","wikidata":"https://www.wikidata.org/wiki/Q82642","display_name":"Blood pressure","level":2,"score":0.4421460032463074},{"id":"https://openalex.org/C19118579","wikidata":"https://www.wikidata.org/wiki/Q786423","display_name":"Frequency domain","level":2,"score":0.4178091287612915},{"id":"https://openalex.org/C197424946","wikidata":"https://www.wikidata.org/wiki/Q1165717","display_name":"Waveform","level":3,"score":0.414132297039032},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.34399545192718506},{"id":"https://openalex.org/C24890656","wikidata":"https://www.wikidata.org/wiki/Q82811","display_name":"Acoustics","level":1,"score":0.11316150426864624},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.10305783152580261},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.09587591886520386},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.08342674374580383},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.08313089609146118},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.07765302062034607},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2019.2933498","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2933498","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08789404.pdf","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"},{"id":"pmh:oai:doaj.org/article:1d2715ef1c554bc890e538d52679b7ce","is_oa":true,"landing_page_url":"https://doaj.org/article/1d2715ef1c554bc890e538d52679b7ce","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 7, Pp 113427-113439 (2019)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2019.2933498","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2933498","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08789404.pdf","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/F4320329174","display_name":"Shenzhen Municipal Science and Technology Innovation Council","ror":"https://ror.org/017n8df75"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2964606899.pdf","grobid_xml":"https://content.openalex.org/works/W2964606899.grobid-xml"},"referenced_works_count":36,"referenced_works":["https://openalex.org/W200905981","https://openalex.org/W1954157498","https://openalex.org/W1964995554","https://openalex.org/W2003899531","https://openalex.org/W2041420156","https://openalex.org/W2042202802","https://openalex.org/W2071081729","https://openalex.org/W2078918819","https://openalex.org/W2080382620","https://openalex.org/W2100110121","https://openalex.org/W2101266901","https://openalex.org/W2105012074","https://openalex.org/W2108584754","https://openalex.org/W2110798204","https://openalex.org/W2116064496","https://openalex.org/W2121071670","https://openalex.org/W2122991268","https://openalex.org/W2136922672","https://openalex.org/W2138857742","https://openalex.org/W2150876123","https://openalex.org/W2160464035","https://openalex.org/W2166451543","https://openalex.org/W2296409840","https://openalex.org/W2527553515","https://openalex.org/W2527556222","https://openalex.org/W2529470480","https://openalex.org/W2543039112","https://openalex.org/W2610189252","https://openalex.org/W2613427209","https://openalex.org/W2742382610","https://openalex.org/W2790385199","https://openalex.org/W4231109964","https://openalex.org/W4247747761","https://openalex.org/W6608164371","https://openalex.org/W6676481782","https://openalex.org/W6680300913"],"related_works":["https://openalex.org/W2024367938","https://openalex.org/W2782295999","https://openalex.org/W2162306796","https://openalex.org/W1970292246","https://openalex.org/W2016162169","https://openalex.org/W4247952185","https://openalex.org/W1895367623","https://openalex.org/W1642462315","https://openalex.org/W2015118744","https://openalex.org/W2005619368"],"abstract_inverted_index":{"In":[0],"general,":[1],"existing":[2,53],"machine":[3],"learning":[4],"based":[5,61,279],"approaches,":[6],"developed":[7],"for":[8,63,108,154,250,255,281],"systolic":[9,125],"and":[10,15,31,112,126,129,157,165,187,192,215,232,252],"diastolic":[11,127,131],"blood":[12,46],"pressure":[13,47],"(SBP":[14],"DBP)":[16],"estimation":[17,65,283],"from":[18,25,72,80,159,163,209,216,289],"oscillometric":[19],"waveforms":[20],"(OWs),":[21],"employ":[22],"features":[23,36,70,79,96,274,286],"extracted":[24,71,94,162,287],"the":[26,43,52,84,88,93,99,178,183,200,204,224,265],"OW":[27,110],"envelope":[28],"(OWE)":[29],"alone":[30],"ignore":[32],"important":[33],"beat-by-beat":[34],"(BBB)":[35],"which":[37,203],"represent":[38],"fundamental":[39],"physical":[40],"properties":[41],"of":[42,83,117,237,247],"entire":[44],"non-invasive":[45],"(NIBP)":[48],"measurement":[49],"system.":[50],"Unlike":[51],"literature,":[54],"this":[55],"paper":[56],"proposes":[57],"a":[58,105,136,148,228,234],"novel":[59,149],"deep-learning":[60,278],"method":[62,153],"BP":[64,282],"trained":[66,270,284],"with":[67,98,271,285],"BBB":[68,95,272],"time-domain":[69,78,273],"OWs.":[73],"First,":[74],"we":[75,103],"extract":[76],"six":[77],"each":[81,109],"beat":[82,111],"OW,":[85],"relative":[86,257],"to":[87,212,219,223,258],"preceding":[89],"beat.":[90],"Second,":[91],"using":[92,233],"along":[97],"corresponding":[100,167],"cuff":[101],"pressures,":[102],"form":[104],"feature":[106,151,160,185],"vector":[107],"locate":[113],"it":[114],"in":[115],"one":[116],"three":[118],"different":[119],"classes,":[120],"namely":[121],"pre-systolic":[122],"(PS),":[123],"between":[124,182],"(BSD)":[128],"after":[130],"(AD).":[132],"We":[133,261],"then":[134,196],"devise":[135],"deep-belief":[137],"network":[138,141,205],"(DBN)-deep":[139],"neural":[140],"(DNN)":[142],"classification":[143,173,268],"model":[144],"as":[145,147],"well":[146],"artificial":[150,184],"extraction":[152],"estimating":[155],"SBP":[156,191,251],"DBP":[158,193,256],"vectors":[161,186],"OWs":[164],"their":[166],"deflation":[168,225],"curves.":[169],"The":[170,190],"proposed":[171,266],"DBN-DNN":[172],"approach":[174],"can":[175,275],"effectively":[176],"learn":[177],"complex":[179],"nonlinear":[180],"relationship":[181],"target":[188],"classes.":[189],"points":[194],"are":[195],"obtained":[197],"by":[198],"mapping":[199],"beats":[201],"at":[202],"output":[206],"sequence":[207],"switches":[208],"PS":[210],"phase":[211,214,218],"BSD":[213,217],"AD":[220],"phase,":[221],"respectively,":[222],"curve.":[226],"Adopting":[227],"5-fold":[229],"cross-validation":[230],"scheme":[231],"data":[235],"base":[236],"350":[238],"NIBP":[239],"recordings":[240],"gave":[241],"an":[242],"average":[243],"mean":[244],"absolute":[245],"error":[246],"1.1\u00b12.9":[248],"mmHg":[249,254],"3.0\u00b15.6":[253],"reference":[259],"values.":[260],"experimentally":[262],"show":[263],"that":[264],"DBN-DNN-based":[267],"algorithm":[269],"outperform":[276],"traditional":[277],"methods":[280],"only":[288],"OWEs.":[290]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
