{"id":"https://openalex.org/W3081853167","doi":"https://doi.org/10.1109/embc44109.2020.9176849","title":"Schr\u00f6dinger Spectrum Based PPG Features for the Estimation of the Arterial Blood Pressure","display_name":"Schr\u00f6dinger Spectrum Based PPG Features for the Estimation of the Arterial Blood Pressure","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3081853167","doi":"https://doi.org/10.1109/embc44109.2020.9176849","mag":"3081853167","pmid":"https://pubmed.ncbi.nlm.nih.gov/33018559"},"language":"en","primary_location":{"id":"doi:10.1109/embc44109.2020.9176849","is_oa":false,"landing_page_url":"https://doi.org/10.1109/embc44109.2020.9176849","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 42nd Annual International Conference of the IEEE Engineering in Medicine &amp; Biology Society (EMBC)","raw_type":"proceedings-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/A5101701450","display_name":"Peihao Li","orcid":"https://orcid.org/0000-0001-5722-0977"},"institutions":[{"id":"https://openalex.org/I71920554","display_name":"King Abdullah University of Science and Technology","ror":"https://ror.org/01q3tbs38","country_code":"SA","type":"education","lineage":["https://openalex.org/I71920554"]}],"countries":["SA"],"is_corresponding":true,"raw_author_name":"Peihao Li","raw_affiliation_strings":["Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), KSA"],"affiliations":[{"raw_affiliation_string":"Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), KSA","institution_ids":["https://openalex.org/I71920554"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5075679869","display_name":"Taous\u2010Meriem Laleg\u2010Kirati","orcid":"https://orcid.org/0000-0001-5944-0121"},"institutions":[{"id":"https://openalex.org/I71920554","display_name":"King Abdullah University of Science and Technology","ror":"https://ror.org/01q3tbs38","country_code":"SA","type":"education","lineage":["https://openalex.org/I71920554"]}],"countries":["SA"],"is_corresponding":false,"raw_author_name":"Taous-Meriem Laleg-Kirati","raw_affiliation_strings":["Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), KSA"],"affiliations":[{"raw_affiliation_string":"Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), KSA","institution_ids":["https://openalex.org/I71920554"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5101701450"],"corresponding_institution_ids":["https://openalex.org/I71920554"],"apc_list":null,"apc_paid":null,"fwci":0.1717,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.46176086,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"7","issue":null,"first_page":"2683","last_page":"2686"},"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":0.9987000226974487,"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":0.9973000288009644,"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":0.9806573987007141},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5844497680664062},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5664734244346619},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.512677788734436},{"id":"https://openalex.org/keywords/standard-deviation","display_name":"Standard deviation","score":0.48832055926322937},{"id":"https://openalex.org/keywords/linear-regression","display_name":"Linear regression","score":0.4827505946159363},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4731912612915039},{"id":"https://openalex.org/keywords/signal-processing","display_name":"Signal processing","score":0.4580589234828949},{"id":"https://openalex.org/keywords/spectral-density-estimation","display_name":"Spectral density estimation","score":0.45737695693969727},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.4353662431240082},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.41896387934684753},{"id":"https://openalex.org/keywords/blood-pressure","display_name":"Blood pressure","score":0.4185311794281006},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.4123651385307312},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3083488941192627},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.24168789386749268},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.22296375036239624},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.20075544714927673},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.14276745915412903},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08386743068695068}],"concepts":[{"id":"https://openalex.org/C116390426","wikidata":"https://www.wikidata.org/wiki/Q7187885","display_name":"Photoplethysmogram","level":3,"score":0.9806573987007141},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5844497680664062},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5664734244346619},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.512677788734436},{"id":"https://openalex.org/C22679943","wikidata":"https://www.wikidata.org/wiki/Q159375","display_name":"Standard deviation","level":2,"score":0.48832055926322937},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.4827505946159363},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4731912612915039},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.4580589234828949},{"id":"https://openalex.org/C30049272","wikidata":"https://www.wikidata.org/wiki/Q6555326","display_name":"Spectral density estimation","level":3,"score":0.45737695693969727},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.4353662431240082},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.41896387934684753},{"id":"https://openalex.org/C84393581","wikidata":"https://www.wikidata.org/wiki/Q82642","display_name":"Blood pressure","level":2,"score":0.4185311794281006},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4123651385307312},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3083488941192627},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.24168789386749268},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.22296375036239624},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.20075544714927673},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.14276745915412903},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08386743068695068},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C102519508","wikidata":"https://www.wikidata.org/wiki/Q6520159","display_name":"Fourier transform","level":2,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"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/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.0},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0}],"mesh":[{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000069553","descriptor_name":"Supervised Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000069553","descriptor_name":"Supervised Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000069553","descriptor_name":"Supervised Machine Learning","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":"D000465","descriptor_name":"Algorithms","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":"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":"D016208","descriptor_name":"Databases, Factual","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016208","descriptor_name":"Databases, Factual","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016208","descriptor_name":"Databases, Factual","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D062186","descriptor_name":"Arterial Pressure","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D062186","descriptor_name":"Arterial Pressure","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D062186","descriptor_name":"Arterial Pressure","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true}],"locations_count":3,"locations":[{"id":"doi:10.1109/embc44109.2020.9176849","is_oa":false,"landing_page_url":"https://doi.org/10.1109/embc44109.2020.9176849","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 42nd Annual International Conference of the IEEE Engineering in Medicine &amp; Biology Society (EMBC)","raw_type":"proceedings-article"},{"id":"pmid:33018559","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/33018559","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":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","raw_type":null},{"id":"pmh:oai:repository.kaust.edu.sa:10754/665144","is_oa":false,"landing_page_url":"http://hdl.handle.net/10754/665144","pdf_url":null,"source":{"id":"https://openalex.org/S4306401596","display_name":"King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I71920554","host_organization_name":"King Abdullah University of Science and Technology","host_organization_lineage":["https://openalex.org/I71920554"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Conference Paper"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4699999988079071,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1528687555","https://openalex.org/W1583452736","https://openalex.org/W1984106284","https://openalex.org/W1991074413","https://openalex.org/W2017307708","https://openalex.org/W2054016564","https://openalex.org/W2083872334","https://openalex.org/W2104995777","https://openalex.org/W2139145457","https://openalex.org/W2145453880","https://openalex.org/W2338236284","https://openalex.org/W2593547679","https://openalex.org/W2892649144","https://openalex.org/W2894890204","https://openalex.org/W2969170900","https://openalex.org/W4243961335","https://openalex.org/W6634805819"],"related_works":["https://openalex.org/W2732360296","https://openalex.org/W1994871954","https://openalex.org/W4297152434","https://openalex.org/W2072858761","https://openalex.org/W1540261775","https://openalex.org/W4200107443","https://openalex.org/W4239033438","https://openalex.org/W4206678752","https://openalex.org/W4311044804","https://openalex.org/W2017783276"],"abstract_inverted_index":{"In":[0],"this":[1,38],"paper,":[2],"photoplethysmogram":[3],"(PPG)":[4],"features":[5,179],"are":[6,30,35,82,126],"combined":[7],"with":[8,61,98,108],"supervised":[9],"machine":[10,130,181],"learning":[11,131,182],"algorithms":[12,20,81,132,183],"to":[13,200],"estimate":[14],"arterial":[15],"blood":[16],"pressure":[17],"(ABP).":[18],"Three":[19],"for":[21,84,113,128,184,196],"the":[22,54,74,85,134,144,155,166,170,177,185,189,194,202],"estimation":[23,137,150,172,187],"of":[24,70,149,169,188],"cuffless":[25,186],"ABP":[26,48,55,86,156,171,190,203],"using":[27],"PPG":[28,33,59,75],"signals":[29,34,60],"compared.":[31],"Since":[32],"measured":[36],"non-invasively,":[37],"method":[39,90],"guarantees":[40],"an":[41],"individuals":[42],"comfort":[43],"while":[44],"not":[45],"omitting":[46],"important":[47],"information.":[49],"The":[50,88],"proposed":[51,89,178],"framework":[52],"predicts":[53],"values":[56],"by":[57],"processing":[58],"semi-classical":[62],"signal":[63,76],"analysis":[64],"(SCSA)":[65],"method,":[66,173],"extracting":[67],"several":[68],"categories":[69],"features,":[71],"which":[72,152],"reflect":[73],"morphology":[77],"variations.":[78],"Then,":[79],"regression":[80,141],"selected":[83],"estimation.":[87],"is":[91],"evaluated":[92,127],"based":[93],"on":[94],"a":[95,162],"virtual":[96],"dataset":[97],"more":[99],"than":[100],"four":[101],"thousand":[102,111],"subjects":[103,112],"and":[104,116,122,136,180],"MIMIC":[105],"II":[106],"database":[107],"over":[109],"eight":[110],"model":[114],"training":[115],"testing.":[117],"Mean":[118],"average":[119],"error":[120],"(MAE)":[121],"standard":[123,146],"deviation":[124],"(STD)":[125],"different":[129],"during":[133],"prediction":[135],"process.":[138],"Multiple":[139],"linear":[140],"(MLR)":[142],"meets":[143],"AAMI":[145],"in":[147,161],"terms":[148],"accuracy,":[151],"proves":[153],"that":[154,176],"can":[157,191],"be":[158],"accurately":[159],"estimated":[160],"nonintrusive":[163],"fashion.":[164],"Given":[165],"easy":[167],"implementation":[168],"we":[174],"regard":[175],"potentially":[192],"provide":[193],"means":[195],"mobile":[197],"healthcare":[198],"equipment":[199],"monitor":[201],"continuously.":[204]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
