{"id":"https://openalex.org/W3091686916","doi":"https://doi.org/10.1109/ijcnn48605.2020.9206890","title":"A cascaded step-temporal attention network for ECG arrhythmia classification","display_name":"A cascaded step-temporal attention network for ECG arrhythmia classification","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3091686916","doi":"https://doi.org/10.1109/ijcnn48605.2020.9206890","mag":"3091686916"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn48605.2020.9206890","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9206890","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","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/A5064029585","display_name":"Yanyun Tao","orcid":"https://orcid.org/0000-0002-6553-7736"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanyun Tao","raw_affiliation_strings":["School of rail transportation, Soochow university, Suzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of rail transportation, Soochow university, Suzhou, China","institution_ids":["https://openalex.org/I3923682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028185175","display_name":"Guoqi Yue","orcid":null},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guoqi Yue","raw_affiliation_strings":["School of rail transportation, Soochow university, Suzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of rail transportation, Soochow university, Suzhou, China","institution_ids":["https://openalex.org/I3923682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108089759","display_name":"Kaixin Wang","orcid":"https://orcid.org/0009-0000-6049-0310"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kaixin Wang","raw_affiliation_strings":["School of rail transportation, Soochow university, Suzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of rail transportation, Soochow university, Suzhou, China","institution_ids":["https://openalex.org/I3923682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100721871","display_name":"Yuzhen Zhang","orcid":"https://orcid.org/0000-0002-7930-5549"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]},{"id":"https://openalex.org/I4210153519","display_name":"First Affiliated Hospital of Soochow University","ror":"https://ror.org/051jg5p78","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210153519"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuzhen Zhang","raw_affiliation_strings":["The First Affiliated Hospital of Soochow University, Suzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The First Affiliated Hospital of Soochow University, Suzhou, China","institution_ids":["https://openalex.org/I3923682","https://openalex.org/I4210153519"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008984910","display_name":"Bin Jiang","orcid":"https://orcid.org/0000-0002-2897-5745"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]},{"id":"https://openalex.org/I4210153519","display_name":"First Affiliated Hospital of Soochow University","ror":"https://ror.org/051jg5p78","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210153519"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bin Jiang","raw_affiliation_strings":["The First Affiliated Hospital of Soochow University, Suzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The First Affiliated Hospital of Soochow University, Suzhou, China","institution_ids":["https://openalex.org/I3923682","https://openalex.org/I4210153519"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"86","issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11021","display_name":"ECG Monitoring and Analysis","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9979000091552734,"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/T12419","display_name":"Phonocardiography and Auscultation Techniques","score":0.9869999885559082,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7113500237464905},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.7081881165504456},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.697234570980072},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5731383562088013},{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.5558058619499207},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5083627104759216},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4852023422718048},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.4755301773548126},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.4687770903110504},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.4521215558052063},{"id":"https://openalex.org/keywords/heartbeat","display_name":"Heartbeat","score":0.4343094825744629},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4146571755409241},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4105517864227295},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4072079360485077}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7113500237464905},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.7081881165504456},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.697234570980072},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5731383562088013},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.5558058619499207},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5083627104759216},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4852023422718048},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.4755301773548126},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.4687770903110504},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.4521215558052063},{"id":"https://openalex.org/C13852961","wikidata":"https://www.wikidata.org/wiki/Q17021880","display_name":"Heartbeat","level":2,"score":0.4343094825744629},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4146571755409241},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4105517864227295},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4072079360485077},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn48605.2020.9206890","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9206890","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Life in Land","score":0.6100000143051147,"id":"https://metadata.un.org/sdg/15"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W1981462270","https://openalex.org/W1983100643","https://openalex.org/W2028117997","https://openalex.org/W2049001544","https://openalex.org/W2101166342","https://openalex.org/W2251133041","https://openalex.org/W2604926040","https://openalex.org/W2748902594","https://openalex.org/W2755308505","https://openalex.org/W2755507768","https://openalex.org/W2767076830","https://openalex.org/W2795340004","https://openalex.org/W2796767721","https://openalex.org/W2802900481","https://openalex.org/W2889838428","https://openalex.org/W4241637638"],"related_works":["https://openalex.org/W2905433371","https://openalex.org/W2888392564","https://openalex.org/W4361806667","https://openalex.org/W2768320620","https://openalex.org/W4293151273","https://openalex.org/W4366990902","https://openalex.org/W4317732970","https://openalex.org/W4388550696","https://openalex.org/W4321636153","https://openalex.org/W4313289487"],"abstract_inverted_index":{"To":[0,77],"improve":[1],"the":[2,9,33,67,79,90,94,117,175],"accuracy":[3,118],"of":[4,25,56,70,119,126,147,178],"arrhythmia":[5,111],"diagnosis":[6],"and":[7,46,93,105,138,142,152,171,181],"reduce":[8],"recheck":[10],"time,":[11],"we":[12],"design":[13],"a":[14,37,57,100],"cascaded":[15],"step-temporal":[16],"attention":[17],"network":[18,150,155,162],"called":[19],"ArrhythmiaNet":[20,82,92,120,164,185,196],"to":[21,174],"classify":[22],"15":[23],"categories":[24],"arrhythmias":[26,201],"on":[27,99],"electrocardiogram":[28],"(ECG)":[29],"signals.":[30],"In":[31,89],"ArrhythmiaNet,":[32],"first":[34],"level":[35,53],"contains":[36],"convolution":[38],"layer":[39],"with":[40,62,158,192],"step-attention,":[41],"which":[42,65],"recognizes":[43],"abnormal":[44,74],"heartbeat":[45],"provides":[47],"morphological":[48],"feature":[49,80],"expression.":[50],"The":[51,113],"second":[52],"is":[54],"composed":[55],"gated":[58],"recurrent":[59,153],"unit":[60],"(GRU)":[61],"temporal":[63,68],"attention,":[64],"mines":[66],"correlation":[69],"long-term":[71,148],"rhythm":[72,75],"for":[73],"judgement.":[76],"share":[78],"expression,":[81],"was":[83,121],"trained":[84],"by":[85],"end-to-end":[86],"multitask":[87],"learning.":[88],"experiment,":[91],"comparison":[95],"algorithms":[96],"were":[97],"tested":[98],"dataset":[101],"(819":[102],"training":[103],"samples":[104],"264":[106],"test":[107],"samples)":[108],"from":[109],"MIT-BIH":[110],"database.":[112],"results":[114],"showed":[115],"that":[116,125,146],"20.3%":[122],"higher":[123,144,169,198],"than":[124,145,190],"support":[127],"vector":[128],"machine":[129],"(SVM),":[130],"Naive":[131],"Bayesian,":[132],"gradient":[133],"boost":[134],"decision":[135],"tree":[136],"(GBDT)":[137],"random":[139],"forest":[140],"(RF),":[141],"8.2%":[143],"memory":[149],"(LSTM)":[151],"neural":[154,161,183],"(RNN).":[156],"Compared":[157,173],"1-dimension":[159],"convolutional":[160],"(1D-CNN),":[163],"obtained":[165],"similar":[166],"overall":[167],"accuracy,":[168],"recall":[170],"precision.":[172],"genetic":[176],"ensemble":[177],"SVM":[179],"classifiers":[180],"evolutionary":[182],"system,":[184],"has":[186,197],"much":[187],"lower":[188],"complexity":[189],"them":[191],"competitive":[193],"accuracy.":[194],"Besides,":[195],"interpretability":[199],"in":[200],"diagnosis.":[202]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
