{"id":"https://openalex.org/W2793045253","doi":"https://doi.org/10.1109/cisp-bmei.2017.8302279","title":"Assessment of photoplethysmogram signal quality based on frequency domain and time series parameters","display_name":"Assessment of photoplethysmogram signal quality based on frequency domain and time series parameters","publication_year":2017,"publication_date":"2017-10-01","ids":{"openalex":"https://openalex.org/W2793045253","doi":"https://doi.org/10.1109/cisp-bmei.2017.8302279","mag":"2793045253"},"language":"en","primary_location":{"id":"doi:10.1109/cisp-bmei.2017.8302279","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cisp-bmei.2017.8302279","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","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/A5100333638","display_name":"Yue Zhang","orcid":"https://orcid.org/0000-0001-5999-0000"},"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":"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"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5111878539","display_name":"Junjun Pan","orcid":null},"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":false,"raw_author_name":"Junjun Pan","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/A5100333638"],"corresponding_institution_ids":["https://openalex.org/I3131625388","https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":0.2549,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.56984095,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11196","display_name":"Non-Invasive Vital Sign Monitoring","score":0.9994999766349792,"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":0.9994999766349792,"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/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9979000091552734,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10745","display_name":"Heart Rate Variability and Autonomic Control","score":0.9929999709129333,"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":0.7213649749755859},{"id":"https://openalex.org/keywords/frequency-domain","display_name":"Frequency domain","score":0.6155986785888672},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.6041359901428223},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5966787338256836},{"id":"https://openalex.org/keywords/time-domain","display_name":"Time domain","score":0.5495395660400391},{"id":"https://openalex.org/keywords/signal-processing","display_name":"Signal processing","score":0.5053728222846985},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4741984009742737},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.46753713488578796},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.44601207971572876},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.4366288483142853},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4066654443740845},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.40187233686447144},{"id":"https://openalex.org/keywords/digital-signal-processing","display_name":"Digital signal processing","score":0.08387267589569092},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.07475742697715759}],"concepts":[{"id":"https://openalex.org/C116390426","wikidata":"https://www.wikidata.org/wiki/Q7187885","display_name":"Photoplethysmogram","level":3,"score":0.7213649749755859},{"id":"https://openalex.org/C19118579","wikidata":"https://www.wikidata.org/wiki/Q786423","display_name":"Frequency domain","level":2,"score":0.6155986785888672},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.6041359901428223},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5966787338256836},{"id":"https://openalex.org/C103824480","wikidata":"https://www.wikidata.org/wiki/Q185889","display_name":"Time domain","level":2,"score":0.5495395660400391},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.5053728222846985},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4741984009742737},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46753713488578796},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.44601207971572876},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.4366288483142853},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4066654443740845},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40187233686447144},{"id":"https://openalex.org/C84462506","wikidata":"https://www.wikidata.org/wiki/Q173142","display_name":"Digital signal processing","level":2,"score":0.08387267589569092},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.07475742697715759},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cisp-bmei.2017.8302279","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cisp-bmei.2017.8302279","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W1989977532","https://openalex.org/W2006803905","https://openalex.org/W2046788142","https://openalex.org/W2056651346","https://openalex.org/W2060845893","https://openalex.org/W2077770566","https://openalex.org/W2139212933","https://openalex.org/W6665704836"],"related_works":["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","https://openalex.org/W1879092539"],"abstract_inverted_index":{"In":[0,15,33],"this":[1,16],"work,":[2],"we":[3,37,50],"describe":[4],"a":[5],"new":[6,127],"method":[7],"for":[8],"assessing":[9],"the":[10,34,39,52,66,76,90,117,123],"photoplethysmogram":[11],"(PPG)":[12],"signal":[13,40,67,91],"quality.":[14,92],"algorithm,":[17],"two":[18],"types":[19],"of":[20,47,65,114],"characteristic":[21,73],"parameters":[22,87],"are":[23],"used,":[24],"including":[25],"frequency":[26,35],"domain":[27],"characteristics":[28],"and":[29,44,59,75,100,120,131],"time":[30,48],"series":[31],"characteristics.":[32],"domain,":[36],"use":[38,51],"power":[41],"spectrum":[42],"characteristics,":[43],"in":[45,139],"terms":[46],"series,":[49],"correlation":[53],"dimension,":[54,56],"box-counting":[55],"fuzzy":[57],"entropy":[58],"Lempei-Ziv":[60],"complexity.":[61],"The":[62,104,126],"quality":[63],"evaluation":[64],"is":[68,82,129],"carried":[69],"out":[70],"with":[71],"each":[72],"parameter,":[74],"support":[77],"vector":[78],"machine":[79],"(SVM)":[80],"model":[81],"used":[83,97],"to":[84,88,98,136],"fuse":[85],"these":[86],"determine":[89],"An":[93],"expert-labeled":[94],"database":[95],"was":[96],"train":[99],"test":[101,118],"our":[102,108],"algorithms.":[103],"results":[105],"show":[106],"that":[107],"algorithm":[109,128],"has":[110],"an":[111],"accuracy":[112],"rate":[113],"96.6%":[115],"on":[116,122],"set":[119],"99.0%":[121],"training":[124],"set.":[125],"accurate":[130],"reliable,":[132],"showing":[133],"strong":[134],"potential":[135],"be":[137],"applied":[138],"practice.":[140]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}