{"id":"https://openalex.org/W2793948665","doi":"https://doi.org/10.1145/3163080.3163109","title":"A Novel Frequency Domain Method for Estimating Blood Pressure from Photoplethysmogram","display_name":"A Novel Frequency Domain Method for Estimating Blood Pressure from Photoplethysmogram","publication_year":2017,"publication_date":"2017-11-27","ids":{"openalex":"https://openalex.org/W2793948665","doi":"https://doi.org/10.1145/3163080.3163109","mag":"2793948665"},"language":"en","primary_location":{"id":"doi:10.1145/3163080.3163109","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3163080.3163109","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 9th International Conference on Signal Processing Systems","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"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/A5100676109","display_name":"Zhanyu Wang","orcid":"https://orcid.org/0009-0002-1761-2057"},"institutions":[{"id":"https://openalex.org/I3131625388","display_name":"University Town of Shenzhen","ror":"https://ror.org/05f5j6225","country_code":"CN","type":"education","lineage":["https://openalex.org/I3131625388"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhanyu Wang","raw_affiliation_strings":["Laboratory of Embedded Systems and Technology, Graduate School at Shenzhen, Tsinghua University, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Laboratory of Embedded Systems and Technology, Graduate School at Shenzhen, Tsinghua University, Shenzhen, China","institution_ids":["https://openalex.org/I3131625388","https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100333638","display_name":"Yue Zhang","orcid":"https://orcid.org/0000-0001-5999-0000"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]},{"id":"https://openalex.org/I3131625388","display_name":"University Town of Shenzhen","ror":"https://ror.org/05f5j6225","country_code":"CN","type":"education","lineage":["https://openalex.org/I3131625388"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yue Zhang","raw_affiliation_strings":["Laboratory of Embedded Systems and Technology, Graduate School at Shenzhen, Tsinghua University, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Laboratory of Embedded Systems and Technology, Graduate School at Shenzhen, Tsinghua University, Shenzhen, China","institution_ids":["https://openalex.org/I3131625388","https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100676109"],"corresponding_institution_ids":["https://openalex.org/I3131625388","https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":0.1275,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.50617737,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"201","last_page":"206"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11196","display_name":"Non-Invasive Vital Sign Monitoring","score":0.9994000196456909,"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":0.9994000196456909,"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/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.9945999979972839,"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":0.9944000244140625,"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":0.9662518501281738},{"id":"https://openalex.org/keywords/blood-pressure","display_name":"Blood pressure","score":0.7777637243270874},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.6244344115257263},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5565377473831177},{"id":"https://openalex.org/keywords/backpropagation","display_name":"Backpropagation","score":0.5372561812400818},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5245328545570374},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5240671634674072},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5137858986854553},{"id":"https://openalex.org/keywords/discrete-cosine-transform","display_name":"Discrete cosine transform","score":0.4225318729877472},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3387807011604309},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.2503056228160858},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.18882212042808533},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.13175949454307556}],"concepts":[{"id":"https://openalex.org/C116390426","wikidata":"https://www.wikidata.org/wiki/Q7187885","display_name":"Photoplethysmogram","level":3,"score":0.9662518501281738},{"id":"https://openalex.org/C84393581","wikidata":"https://www.wikidata.org/wiki/Q82642","display_name":"Blood pressure","level":2,"score":0.7777637243270874},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.6244344115257263},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5565377473831177},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.5372561812400818},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5245328545570374},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5240671634674072},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5137858986854553},{"id":"https://openalex.org/C2221639","wikidata":"https://www.wikidata.org/wiki/Q2877","display_name":"Discrete cosine transform","level":3,"score":0.4225318729877472},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3387807011604309},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.2503056228160858},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.18882212042808533},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.13175949454307556},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","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":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3163080.3163109","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3163080.3163109","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 9th International Conference on Signal Processing Systems","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3","score":0.7900000214576721}],"awards":[{"id":"https://openalex.org/G4997497501","display_name":null,"funder_award_id":"No.61571268","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1971735090","https://openalex.org/W2023825153","https://openalex.org/W2039708501","https://openalex.org/W2063788091","https://openalex.org/W2088204643","https://openalex.org/W2108839717","https://openalex.org/W2111256021","https://openalex.org/W2117482623","https://openalex.org/W2119557624","https://openalex.org/W2162800060","https://openalex.org/W2169010496","https://openalex.org/W2475064587","https://openalex.org/W4214481965"],"related_works":["https://openalex.org/W2136054869","https://openalex.org/W2738889589","https://openalex.org/W2104912729","https://openalex.org/W1591194399","https://openalex.org/W2075600602","https://openalex.org/W2379874775","https://openalex.org/W2179998186","https://openalex.org/W3159598895","https://openalex.org/W2096356999","https://openalex.org/W2187285467"],"abstract_inverted_index":{"A":[0],"novel":[1],"method":[2,134],"of":[3,17,24,32,67,73],"estimating":[4],"blood":[5,40,45,52,94,141],"pressure":[6,41,46,53,142],"(BP)":[7],"from":[8,49,124],"Photoplethysmogram":[9],"(PPG)":[10],"is":[11,57],"provided.":[12],"The":[13,96,128],"first":[14],"15":[15],"points":[16],"the":[18,25,33,38,50,60,68,71],"discrete":[19],"cosine":[20],"transform":[21],"(DCT)":[22],"sequence":[23],"PPG":[26,61,116],"signal":[27,55,62],"are":[28,63,82],"trained":[29],"as":[30,65,84],"inputs":[31],"Backpropagation":[34],"neural":[35],"network":[36],"(BPNN),":[37],"systolic":[39],"(SBP)":[42],"and":[43,109,111],"diastolic":[44],"(DBP)":[47],"extracted":[48,123],"Arterial":[51],"(ABP)":[54],"which":[56],"corresponding":[58,119],"to":[59,87],"used":[64,103],"outputs":[66],"BPNN.":[69],"Combining":[70],"idea":[72],"AdaBoost":[74],"algorithm,":[75],"10":[76],"BPNN":[77],"with":[78,118],"different":[79],"initial":[80],"values":[81],"chosen":[83],"\"weak":[85],"predictors\"":[86],"form":[88],"a":[89],"\"strong":[90],"predictor\"":[91],"for":[92,107],"predicting":[93],"pressure.":[95],"PhysioNet/CinC":[97],"Challenge":[98],"2010":[99],"data":[100,126],"set":[101],"was":[102],"in":[104,136],"this":[105,125,133,137],"work":[106],"training":[108],"testing":[110],"more":[112],"than":[113],"10,000":[114],"separate":[115],"heartbeats":[117],"ABP":[120],"signals":[121],"were":[122],"set.":[127],"experimental":[129],"results":[130],"show":[131],"that":[132],"provided":[135],"article":[138],"can":[139],"predict":[140],"effectively.":[143]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
