{"id":"https://openalex.org/W4319586770","doi":"https://doi.org/10.1109/dsaa54385.2022.10032354","title":"PearNet: A Pearson Correlation-based Graph Attention Network for Sleep Stage Recognition","display_name":"PearNet: A Pearson Correlation-based Graph Attention Network for Sleep Stage Recognition","publication_year":2022,"publication_date":"2022-10-13","ids":{"openalex":"https://openalex.org/W4319586770","doi":"https://doi.org/10.1109/dsaa54385.2022.10032354"},"language":"en","primary_location":{"id":"doi:10.1109/dsaa54385.2022.10032354","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dsaa54385.2022.10032354","pdf_url":null,"source":{"id":"https://openalex.org/S4363608340","display_name":"2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5061888850","display_name":"Jianchao Lu","orcid":"https://orcid.org/0000-0003-0788-1448"},"institutions":[{"id":"https://openalex.org/I99043593","display_name":"Macquarie University","ror":"https://ror.org/01sf06y89","country_code":"AU","type":"education","lineage":["https://openalex.org/I99043593"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Jianchao Lu","raw_affiliation_strings":["Macquarie University,School of Computing,Sydney,Australia","School of Computing, Macquarie University, Sydney, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Macquarie University,School of Computing,Sydney,Australia","institution_ids":["https://openalex.org/I99043593"]},{"raw_affiliation_string":"School of Computing, Macquarie University, Sydney, Australia","institution_ids":["https://openalex.org/I99043593"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028521554","display_name":"Yuzhe Tian","orcid":"https://orcid.org/0000-0002-5742-7414"},"institutions":[{"id":"https://openalex.org/I99043593","display_name":"Macquarie University","ror":"https://ror.org/01sf06y89","country_code":"AU","type":"education","lineage":["https://openalex.org/I99043593"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Yuzhe Tian","raw_affiliation_strings":["Macquarie University,School of Computing,Sydney,Australia","School of Computing, Macquarie University, Sydney, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Macquarie University,School of Computing,Sydney,Australia","institution_ids":["https://openalex.org/I99043593"]},{"raw_affiliation_string":"School of Computing, Macquarie University, Sydney, Australia","institution_ids":["https://openalex.org/I99043593"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100375612","display_name":"Shuang Wang","orcid":"https://orcid.org/0000-0003-3405-5942"},"institutions":[{"id":"https://openalex.org/I99043593","display_name":"Macquarie University","ror":"https://ror.org/01sf06y89","country_code":"AU","type":"education","lineage":["https://openalex.org/I99043593"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Shuang Wang","raw_affiliation_strings":["Macquarie University,School of Computing,Sydney,Australia","School of Computing, Macquarie University, Sydney, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Macquarie University,School of Computing,Sydney,Australia","institution_ids":["https://openalex.org/I99043593"]},{"raw_affiliation_string":"School of Computing, Macquarie University, Sydney, Australia","institution_ids":["https://openalex.org/I99043593"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080744092","display_name":"Quan Z. Sheng","orcid":"https://orcid.org/0000-0002-3326-4147"},"institutions":[{"id":"https://openalex.org/I99043593","display_name":"Macquarie University","ror":"https://ror.org/01sf06y89","country_code":"AU","type":"education","lineage":["https://openalex.org/I99043593"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Michael Sheng","raw_affiliation_strings":["Macquarie University,School of Computing,Sydney,Australia","School of Computing, Macquarie University, Sydney, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Macquarie University,School of Computing,Sydney,Australia","institution_ids":["https://openalex.org/I99043593"]},{"raw_affiliation_string":"School of Computing, Macquarie University, Sydney, Australia","institution_ids":["https://openalex.org/I99043593"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081182489","display_name":"Xi Zheng","orcid":"https://orcid.org/0000-0002-2572-2355"},"institutions":[{"id":"https://openalex.org/I99043593","display_name":"Macquarie University","ror":"https://ror.org/01sf06y89","country_code":"AU","type":"education","lineage":["https://openalex.org/I99043593"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Xi Zheng","raw_affiliation_strings":["Macquarie University,School of Computing,Sydney,Australia","School of Computing, Macquarie University, Sydney, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Macquarie University,School of Computing,Sydney,Australia","institution_ids":["https://openalex.org/I99043593"]},{"raw_affiliation_string":"School of Computing, Macquarie University, Sydney, Australia","institution_ids":["https://openalex.org/I99043593"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I99043593"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9998999834060669,"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/T10985","display_name":"Sleep and Wakefulness Research","score":0.9945999979972839,"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/T11373","display_name":"Sleep and Work-Related Fatigue","score":0.9840999841690063,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7754859924316406},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.6757148504257202},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.6502998471260071},{"id":"https://openalex.org/keywords/pearson-product-moment-correlation-coefficient","display_name":"Pearson product-moment correlation coefficient","score":0.5897659659385681},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5672786831855774},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.551230251789093},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5062669515609741},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.49941062927246094},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.46490561962127686},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4613810181617737},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.414361834526062},{"id":"https://openalex.org/keywords/graph-theory","display_name":"Graph theory","score":0.4107726812362671},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.38969215750694275},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.2427111566066742}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7754859924316406},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.6757148504257202},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6502998471260071},{"id":"https://openalex.org/C55078378","wikidata":"https://www.wikidata.org/wiki/Q1136628","display_name":"Pearson product-moment correlation coefficient","level":2,"score":0.5897659659385681},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5672786831855774},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.551230251789093},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5062669515609741},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.49941062927246094},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.46490561962127686},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4613810181617737},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.414361834526062},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.4107726812362671},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38969215750694275},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2427111566066742},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/dsaa54385.2022.10032354","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dsaa54385.2022.10032354","pdf_url":null,"source":{"id":"https://openalex.org/S4363608340","display_name":"2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320334704","display_name":"Australian Research Council","ror":"https://ror.org/05mmh0f86"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W1169679103","https://openalex.org/W1599825994","https://openalex.org/W1990729619","https://openalex.org/W1998486151","https://openalex.org/W2017689092","https://openalex.org/W2021589706","https://openalex.org/W2048219658","https://openalex.org/W2106210113","https://openalex.org/W2133938361","https://openalex.org/W2604096629","https://openalex.org/W2752782242","https://openalex.org/W2754927243","https://openalex.org/W2769340102","https://openalex.org/W2787740662","https://openalex.org/W2792764867","https://openalex.org/W2805033630","https://openalex.org/W2893892260","https://openalex.org/W2914393402","https://openalex.org/W2914895697","https://openalex.org/W2920016582","https://openalex.org/W2965725058","https://openalex.org/W2977833371","https://openalex.org/W2996952104","https://openalex.org/W2997591099","https://openalex.org/W2997848713","https://openalex.org/W3034369844","https://openalex.org/W3158818505","https://openalex.org/W3188349400","https://openalex.org/W3190152617","https://openalex.org/W4243448215","https://openalex.org/W6606169510","https://openalex.org/W6738964360","https://openalex.org/W6748402259","https://openalex.org/W6749825310"],"related_works":["https://openalex.org/W4225394202","https://openalex.org/W4298287631","https://openalex.org/W2953061907","https://openalex.org/W1847088711","https://openalex.org/W3036642985","https://openalex.org/W3032952384","https://openalex.org/W3017902212","https://openalex.org/W2964335273","https://openalex.org/W2982145560","https://openalex.org/W2969450769"],"abstract_inverted_index":{"Sleep":[0],"stage":[1],"recognition":[2],"is":[3,124],"crucial":[4],"for":[5],"assessing":[6],"sleep":[7],"and":[8,20,119,138],"diagnosing":[9],"chronic":[10],"diseases.":[11],"Deep":[12],"learning":[13,36],"models,":[14],"such":[15],"as":[16,29,97],"Convolutional":[17],"Neural":[18,22],"Networks":[19],"Recurrent":[21],"Networks,":[23],"are":[24,105],"trained":[25],"using":[26],"grid":[27],"data":[28],"input,":[30],"making":[31],"them":[32],"not":[33],"capable":[34],"of":[35,56,76],"relationships":[37,73],"in":[38],"non-Euclidean":[39],"spaces.":[40],"Graph-based":[41],"deep":[42],"models":[43,65],"have":[44],"been":[45],"developed":[46],"to":[47,70,100,127],"address":[48],"this":[49,85,101],"issue":[50],"when":[51],"investigating":[52],"the":[53,64,71,109,121,136,145],"external":[54],"relationship":[55],"electrode":[57,77],"signals":[58,78],"across":[59],"different":[60],"brain":[61,82],"regions.":[62],"However,":[63],"cannot":[66],"solve":[67],"problems":[68],"related":[69],"internal":[72],"between":[74],"segments":[75],"within":[79],"a":[80,89,98,114],"specific":[81],"region.":[83],"In":[84],"study,":[86],"we":[87],"propose":[88],"Pearson":[90],"correlation-based":[91],"graph":[92,122],"attention":[93],"network,":[94],"called":[95],"PearNet,":[96],"solution":[99],"problem.":[102],"Graph":[103],"nodes":[104],"generated":[106],"based":[107],"on":[108,132,135],"spatial-temporal":[110],"features":[111],"extracted":[112],"by":[113],"hierarchical":[115],"feature":[116],"extraction":[117],"method,":[118],"then":[120],"structure":[123],"learned":[125],"adaptively":[126],"build":[128],"node":[129],"connections.":[130],"Based":[131],"our":[133],"experiments":[134],"Sleep-EDF-20":[137],"Sleep-EDF-78":[139],"datasets,":[140],"PearNet":[141],"performs":[142],"better":[143],"than":[144],"state-of-the-art":[146],"baselines.":[147]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
