{"id":"https://openalex.org/W2990196092","doi":"https://doi.org/10.1109/smc.2019.8913905","title":"Multi-class Arrhythmia Detection based on Neural Network with Multi-stage Features Fusion","display_name":"Multi-class Arrhythmia Detection based on Neural Network with Multi-stage Features Fusion","publication_year":2019,"publication_date":"2019-10-01","ids":{"openalex":"https://openalex.org/W2990196092","doi":"https://doi.org/10.1109/smc.2019.8913905","mag":"2990196092"},"language":"en","primary_location":{"id":"doi:10.1109/smc.2019.8913905","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc.2019.8913905","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)","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/A5065798206","display_name":"Ruxin Wang","orcid":"https://orcid.org/0000-0003-4772-3284"},"institutions":[{"id":"https://openalex.org/I4210145761","display_name":"Shenzhen Institutes of Advanced Technology","ror":"https://ror.org/04gh4er46","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210145761"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ruxin Wang","raw_affiliation_strings":["Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China","institution_ids":["https://openalex.org/I4210145761"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077933576","display_name":"Qihang Yao","orcid":"https://orcid.org/0000-0001-5091-3850"},"institutions":[{"id":"https://openalex.org/I4210145761","display_name":"Shenzhen Institutes of Advanced Technology","ror":"https://ror.org/04gh4er46","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210145761"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qihang Yao","raw_affiliation_strings":["Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China","institution_ids":["https://openalex.org/I4210145761"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044447422","display_name":"Xiaomao Fan","orcid":"https://orcid.org/0000-0001-8160-1294"},"institutions":[{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Xiaomao Fan","raw_affiliation_strings":["School of Data Science, City University of Hong Kong, Hong Kong, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Data Science, City University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I168719708"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100339233","display_name":"Ye Li","orcid":"https://orcid.org/0000-0002-5351-8546"},"institutions":[{"id":"https://openalex.org/I4210145761","display_name":"Shenzhen Institutes of Advanced Technology","ror":"https://ror.org/04gh4er46","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210145761"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ye Li","raw_affiliation_strings":["Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China","institution_ids":["https://openalex.org/I4210145761"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":37,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"4082","last_page":"4087"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11021","display_name":"ECG Monitoring and Analysis","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T11021","display_name":"ECG Monitoring and Analysis","score":1.0,"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"}},{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9986000061035156,"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/T12419","display_name":"Phonocardiography and Auscultation Techniques","score":0.9686999917030334,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory 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/computer-science","display_name":"Computer science","score":0.7634470462799072},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6915433406829834},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6252394914627075},{"id":"https://openalex.org/keywords/fuse","display_name":"Fuse (electrical)","score":0.6201052665710449},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6049426198005676},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5241125822067261},{"id":"https://openalex.org/keywords/multiclass-classification","display_name":"Multiclass classification","score":0.5214211940765381},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5061773657798767},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.4843774735927582},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4707161784172058},{"id":"https://openalex.org/keywords/cardiac-arrhythmia","display_name":"Cardiac arrhythmia","score":0.43236786127090454},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.4241911470890045},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.22905239462852478},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.10339576005935669},{"id":"https://openalex.org/keywords/atrial-fibrillation","display_name":"Atrial fibrillation","score":0.10142666101455688}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7634470462799072},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6915433406829834},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6252394914627075},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.6201052665710449},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6049426198005676},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5241125822067261},{"id":"https://openalex.org/C123860398","wikidata":"https://www.wikidata.org/wiki/Q6934605","display_name":"Multiclass classification","level":3,"score":0.5214211940765381},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5061773657798767},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.4843774735927582},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4707161784172058},{"id":"https://openalex.org/C2988455589","wikidata":"https://www.wikidata.org/wiki/Q189331","display_name":"Cardiac arrhythmia","level":3,"score":0.43236786127090454},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.4241911470890045},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.22905239462852478},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10339576005935669},{"id":"https://openalex.org/C2779161974","wikidata":"https://www.wikidata.org/wiki/Q815819","display_name":"Atrial fibrillation","level":2,"score":0.10142666101455688},{"id":"https://openalex.org/C164705383","wikidata":"https://www.wikidata.org/wiki/Q10379","display_name":"Cardiology","level":1,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/smc.2019.8913905","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc.2019.8913905","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.47999998927116394,"id":"https://metadata.un.org/sdg/10"},{"display_name":"Peace, Justice and strong institutions","score":0.46000000834465027,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W1986689884","https://openalex.org/W1996894363","https://openalex.org/W2025508891","https://openalex.org/W2082589768","https://openalex.org/W2100537461","https://openalex.org/W2101331317","https://openalex.org/W2103308415","https://openalex.org/W2127854713","https://openalex.org/W2138018679","https://openalex.org/W2171305551","https://openalex.org/W2186910770","https://openalex.org/W2194775991","https://openalex.org/W2251133041","https://openalex.org/W2550553598","https://openalex.org/W2625625371","https://openalex.org/W2702116941","https://openalex.org/W2747685395","https://openalex.org/W2747849569","https://openalex.org/W2763160469","https://openalex.org/W2884585870","https://openalex.org/W2888456553","https://openalex.org/W2898941944","https://openalex.org/W2901226889","https://openalex.org/W2902644322","https://openalex.org/W2909762442","https://openalex.org/W2998508940","https://openalex.org/W3104523752","https://openalex.org/W6637373629","https://openalex.org/W6656594098","https://openalex.org/W6728925852","https://openalex.org/W6743440100"],"related_works":["https://openalex.org/W3000097931","https://openalex.org/W2354322770","https://openalex.org/W4237547500","https://openalex.org/W1570848052","https://openalex.org/W2373192430","https://openalex.org/W4239268388","https://openalex.org/W1537496349","https://openalex.org/W2379407973","https://openalex.org/W4243305035","https://openalex.org/W2909974158"],"abstract_inverted_index":{"Automated":[0],"electrocardiogram":[1],"(ECG)":[2],"analysis":[3],"for":[4,30,65,76,101,111],"arrhythmia":[5,32],"detection":[6,33],"plays":[7],"a":[8,24],"critical":[9],"role":[10],"in":[11,133],"early":[12],"prevention":[13],"and":[14,46,91,119,140],"diagnosis":[15],"of":[16,43,59,98,131,135,138,149],"cardiovascular":[17],"diseases.":[18],"In":[19],"this":[20],"paper,":[21],"we":[22,50],"proposed":[23,109,151],"novel":[25],"end-to-end":[26],"deep":[27],"learning":[28],"method":[29,110],"multiclass":[31],"with":[34,122],"multiple":[35,44],"stage":[36],"features":[37,60,80],"fusion.":[38],"The":[39,143],"network":[40,100],"is":[41,104],"composed":[42],"convolution":[45],"attention":[47,72,89],"module.":[48],"Specifically,":[49],"use":[51],"skip":[52],"connection":[53],"operation":[54],"to":[55],"fuse":[56],"different":[57,63,84],"levels":[58],"extracted":[61],"at":[62,82],"stages":[64],"target":[66],"task":[67],"processing.":[68],"And":[69],"the":[70,79,83,88,95,99,108,147,150],"channel-wise":[71],"modules":[73],"are":[74],"adopted":[75],"effectively":[77],"extracting":[78],"learned":[81],"stages.":[85],"By":[86],"combining":[87],"module":[90],"convolutional":[92],"neural":[93],"network,":[94],"discrimination":[96],"power":[97],"ECG":[102,112,117],"classification":[103,113,134],"improved.":[105],"We":[106],"demonstrate":[107],"on":[114],"an":[115,128],"open":[116],"dataset":[118],"compare":[120],"it":[121],"some":[123],"state-of-the-art":[124],"methods,":[125],"which":[126],"achieves":[127],"average":[129],"F1-score":[130],"81.3%":[132],"8":[136],"types":[137],"arrhythmias":[139],"sinus":[141],"rhythm.":[142],"experimental":[144],"results":[145],"convince":[146],"efficiency":[148],"method.":[152]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":10},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":8},{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
