{"id":"https://openalex.org/W2997678843","doi":"https://doi.org/10.1109/access.2019.2961139","title":"Intelligent Emotion Detection Method Based on Deep Learning in Medical and Health Data","display_name":"Intelligent Emotion Detection Method Based on Deep Learning in Medical and Health Data","publication_year":2019,"publication_date":"2019-12-23","ids":{"openalex":"https://openalex.org/W2997678843","doi":"https://doi.org/10.1109/access.2019.2961139","mag":"2997678843"},"language":"en","primary_location":{"id":"doi:10.1109/access.2019.2961139","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2961139","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/08937486.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/ielx7/6287639/8948470/08937486.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101892618","display_name":"Jianqiang Xu","orcid":"https://orcid.org/0000-0003-0449-4506"},"institutions":[{"id":"https://openalex.org/I143593769","display_name":"East China University of Science and Technology","ror":"https://ror.org/01vyrm377","country_code":"CN","type":"education","lineage":["https://openalex.org/I143593769"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianqiang Xu","raw_affiliation_strings":["School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0003-0449-4506","affiliations":[{"raw_affiliation_string":"School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China","institution_ids":["https://openalex.org/I143593769"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007899204","display_name":"Zhujiao Hu","orcid":"https://orcid.org/0000-0002-5600-1833"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]},{"id":"https://openalex.org/I4210132426","display_name":"Shanghai Fudan Microelectronics (China)","ror":"https://ror.org/02vfj3j86","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210132426"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhujiao Hu","raw_affiliation_strings":["School of Microelectronics, Fudan University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-5600-1833","affiliations":[{"raw_affiliation_string":"School of Microelectronics, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I4210132426","https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039817389","display_name":"Junzhong Zou","orcid":"https://orcid.org/0000-0001-8119-8503"},"institutions":[{"id":"https://openalex.org/I143593769","display_name":"East China University of Science and Technology","ror":"https://ror.org/01vyrm377","country_code":"CN","type":"education","lineage":["https://openalex.org/I143593769"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junzhong Zou","raw_affiliation_strings":["School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0001-8119-8503","affiliations":[{"raw_affiliation_string":"School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China","institution_ids":["https://openalex.org/I143593769"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5013109084","display_name":"Anqi Bi","orcid":"https://orcid.org/0000-0001-6831-3933"},"institutions":[{"id":"https://openalex.org/I21741975","display_name":"Suzhou University of Technology","ror":"https://ror.org/05g6ben79","country_code":"CN","type":"education","lineage":["https://openalex.org/I21741975"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Anqi Bi","raw_affiliation_strings":["School of Computer Science and Engineering, Changshu Institute of Technology, Suzhou, China"],"raw_orcid":"https://orcid.org/0000-0001-6831-3933","affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Changshu Institute of Technology, Suzhou, China","institution_ids":["https://openalex.org/I21741975"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"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":2.6352,"has_fulltext":true,"cited_by_count":36,"citation_normalized_percentile":{"value":0.90153037,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":"8","issue":null,"first_page":"3802","last_page":"3811"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11373","display_name":"Sleep and Work-Related Fatigue","score":0.9593999981880188,"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"}},"topics":[{"id":"https://openalex.org/T11373","display_name":"Sleep and Work-Related Fatigue","score":0.9593999981880188,"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"}},{"id":"https://openalex.org/T14413","display_name":"Advanced Technologies in Various Fields","score":0.9326000213623047,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7111998200416565},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6699036359786987},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6331650614738464},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6300091743469238},{"id":"https://openalex.org/keywords/modal","display_name":"Modal","score":0.5668274760246277},{"id":"https://openalex.org/keywords/abnormality","display_name":"Abnormality","score":0.4813902676105499},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.43082892894744873},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.41321444511413574},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.387384295463562},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35269391536712646},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.3370705544948578},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.13935914635658264}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7111998200416565},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6699036359786987},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6331650614738464},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6300091743469238},{"id":"https://openalex.org/C71139939","wikidata":"https://www.wikidata.org/wiki/Q910194","display_name":"Modal","level":2,"score":0.5668274760246277},{"id":"https://openalex.org/C50965678","wikidata":"https://www.wikidata.org/wiki/Q2724302","display_name":"Abnormality","level":2,"score":0.4813902676105499},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.43082892894744873},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.41321444511413574},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.387384295463562},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35269391536712646},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3370705544948578},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.13935914635658264},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.0},{"id":"https://openalex.org/C188027245","wikidata":"https://www.wikidata.org/wiki/Q750446","display_name":"Polymer chemistry","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2019.2961139","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2961139","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/08937486.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:858e6025cc9d4814b0a0c8bc9dfbceb2","is_oa":true,"landing_page_url":"https://doaj.org/article/858e6025cc9d4814b0a0c8bc9dfbceb2","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 8, Pp 3802-3811 (2020)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2019.2961139","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2961139","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/08937486.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":[{"display_name":"Quality Education","score":0.49000000953674316,"id":"https://metadata.un.org/sdg/4"}],"awards":[{"id":"https://openalex.org/G7810522312","display_name":null,"funder_award_id":"18kjb5200001","funder_id":"https://openalex.org/F4320321410","funder_display_name":"Jiangsu University"}],"funders":[{"id":"https://openalex.org/F4320321410","display_name":"Jiangsu University","ror":"https://ror.org/03jc41j30"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2997678843.pdf","grobid_xml":"https://content.openalex.org/works/W2997678843.grobid-xml"},"referenced_works_count":28,"referenced_works":["https://openalex.org/W32709953","https://openalex.org/W1968539455","https://openalex.org/W2009781958","https://openalex.org/W2017989296","https://openalex.org/W2024122372","https://openalex.org/W2032389912","https://openalex.org/W2037057652","https://openalex.org/W2065804065","https://openalex.org/W2075366286","https://openalex.org/W2100804571","https://openalex.org/W2603422184","https://openalex.org/W2612914888","https://openalex.org/W2752077325","https://openalex.org/W2792244469","https://openalex.org/W2802254245","https://openalex.org/W2804252870","https://openalex.org/W2808960870","https://openalex.org/W2813353782","https://openalex.org/W2900570788","https://openalex.org/W2909891325","https://openalex.org/W2924475988","https://openalex.org/W2939646477","https://openalex.org/W2951398599","https://openalex.org/W2952468414","https://openalex.org/W4294339611","https://openalex.org/W6656567990","https://openalex.org/W6744481211","https://openalex.org/W6764449361"],"related_works":["https://openalex.org/W4247543202","https://openalex.org/W4243456421","https://openalex.org/W4240842027","https://openalex.org/W2417397217","https://openalex.org/W2355857550","https://openalex.org/W3093256375","https://openalex.org/W1841421040","https://openalex.org/W2896815346","https://openalex.org/W3028882978","https://openalex.org/W1487766990"],"abstract_inverted_index":{"Emotional":[0],"abnormality":[1],"may":[2],"be":[3],"brought":[4],"out":[5],"by":[6,96,114,135],"physiological":[7],"fatigue.":[8,154],"In":[9,64],"order":[10],"to":[11,74],"solve":[12],"the":[13,36,47,65,89,106,136,140],"problem,":[14],"an":[15,144],"emotion":[16],"detection":[17,60],"method":[18],"based":[19],"on":[20],"deep":[21],"learning":[22,93,110],"in":[23,30,103,121,151],"medical":[24],"and":[25,46,80],"health":[26],"data":[27,57,78,125],"is":[28,42,62,72,101,118,133],"proposed":[29,141],"this":[31],"paper.":[32],"First":[33],"of":[34,39,49,92,109,147],"all,":[35],"related":[37],"content":[38],"emotional":[40,50,58,81,85,111,130,153],"fatigue":[41,51,59,86],"studied.":[43],"The":[44],"concept":[45],"classification":[48],"are":[52,127],"introduced.":[53],"Then,":[54],"a":[55],"multi-modal":[56,124],"system":[61],"designed.":[63],"system,":[66],"multi-channel":[67,97],"convolutional":[68,98,115],"aotoencoder":[69,99,116],"neural":[70],"network":[71,90,107],"used":[73],"extract":[75],"electrocardiograms":[76],"(ECG)":[77],"features":[79,83,95,113,126],"text":[82,112],"for":[84,129],"detection.":[87,131],"Secondly,":[88],"structure":[91,108],"ECG":[94],"model":[100,117,142],"introduced":[102],"detail.":[104,122],"And":[105],"also":[119],"described":[120],"Finally,":[123],"combined":[128],"It":[132],"shown":[134],"experimental":[137],"results":[138],"that":[139],"has":[143],"average":[145],"accuracy":[146],"more":[148],"than":[149],"85%":[150],"predicting":[152]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":9},{"year":2021,"cited_by_count":4}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}