{"id":"https://openalex.org/W4206518314","doi":"https://doi.org/10.23919/cinc53138.2021.9662739","title":"Robust and Task-Aware Training of Deep Residual Networks for Varying-Lead ECG Classification","display_name":"Robust and Task-Aware Training of Deep Residual Networks for Varying-Lead ECG Classification","publication_year":2021,"publication_date":"2021-09-13","ids":{"openalex":"https://openalex.org/W4206518314","doi":"https://doi.org/10.23919/cinc53138.2021.9662739"},"language":"en","primary_location":{"id":"doi:10.23919/cinc53138.2021.9662739","is_oa":false,"landing_page_url":"https://doi.org/10.23919/cinc53138.2021.9662739","pdf_url":null,"source":{"id":"https://openalex.org/S4363605378","display_name":"2021 Computing in Cardiology (CinC)","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":"2021 Computing in Cardiology (CinC)","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/A5083848214","display_name":"Hansheng Ren","orcid":null},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":true,"raw_author_name":"Hansheng Ren","raw_affiliation_strings":["National University of Singapore"],"affiliations":[{"raw_affiliation_string":"National University of Singapore","institution_ids":["https://openalex.org/I165932596"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009336048","display_name":"Miao Xiong","orcid":"https://orcid.org/0009-0008-6895-1298"},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Miao Xiong","raw_affiliation_strings":["National University of Singapore"],"affiliations":[{"raw_affiliation_string":"National University of Singapore","institution_ids":["https://openalex.org/I165932596"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5065675832","display_name":"Bryan Hooi","orcid":"https://orcid.org/0000-0002-5645-1754"},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Bryan Hooi","raw_affiliation_strings":["National University of Singapore"],"affiliations":[{"raw_affiliation_string":"National University of Singapore","institution_ids":["https://openalex.org/I165932596"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5083848214"],"corresponding_institution_ids":["https://openalex.org/I165932596"],"apc_list":null,"apc_paid":null,"fwci":1.0354,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.73982301,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"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":0.9868000149726868,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9740999937057495,"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/cross-entropy","display_name":"Cross entropy","score":0.6660686731338501},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6385201215744019},{"id":"https://openalex.org/keywords/test-set","display_name":"Test set","score":0.6199973821640015},{"id":"https://openalex.org/keywords/lead","display_name":"Lead (geology)","score":0.595197319984436},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.5942243337631226},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5833669900894165},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.5569460391998291},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.53143310546875},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5053749680519104},{"id":"https://openalex.org/keywords/binary-classification","display_name":"Binary classification","score":0.4919452369213104},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.47943204641342163},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4579623341560364},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.4509083032608032},{"id":"https://openalex.org/keywords/f1-score","display_name":"F1 score","score":0.4402483403682709},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.434828519821167},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4231800436973572},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.37979769706726074},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.157136470079422},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.15187454223632812},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.13963866233825684},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12244147062301636}],"concepts":[{"id":"https://openalex.org/C167981619","wikidata":"https://www.wikidata.org/wiki/Q1685498","display_name":"Cross entropy","level":3,"score":0.6660686731338501},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6385201215744019},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.6199973821640015},{"id":"https://openalex.org/C2777093003","wikidata":"https://www.wikidata.org/wiki/Q6508345","display_name":"Lead (geology)","level":2,"score":0.595197319984436},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.5942243337631226},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5833669900894165},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.5569460391998291},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.53143310546875},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5053749680519104},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.4919452369213104},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.47943204641342163},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4579623341560364},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.4509083032608032},{"id":"https://openalex.org/C148524875","wikidata":"https://www.wikidata.org/wiki/Q6975395","display_name":"F1 score","level":2,"score":0.4402483403682709},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.434828519821167},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4231800436973572},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.37979769706726074},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.157136470079422},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.15187454223632812},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.13963866233825684},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12244147062301636},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"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/C114793014","wikidata":"https://www.wikidata.org/wiki/Q52109","display_name":"Geomorphology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/cinc53138.2021.9662739","is_oa":false,"landing_page_url":"https://doi.org/10.23919/cinc53138.2021.9662739","pdf_url":null,"source":{"id":"https://openalex.org/S4363605378","display_name":"2021 Computing in Cardiology (CinC)","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":"2021 Computing in Cardiology (CinC)","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":17,"referenced_works":["https://openalex.org/W1677182931","https://openalex.org/W2752782242","https://openalex.org/W2765407302","https://openalex.org/W2888456553","https://openalex.org/W2955425717","https://openalex.org/W2996959172","https://openalex.org/W3008167346","https://openalex.org/W3027572331","https://openalex.org/W3098699929","https://openalex.org/W3121053052","https://openalex.org/W4206135956","https://openalex.org/W6745136726","https://openalex.org/W6762718338","https://openalex.org/W6774025570","https://openalex.org/W6781876278","https://openalex.org/W6788939948","https://openalex.org/W6807079463"],"related_works":["https://openalex.org/W3037097571","https://openalex.org/W1999699871","https://openalex.org/W4225124612","https://openalex.org/W2043806667","https://openalex.org/W2021633306","https://openalex.org/W2006801911","https://openalex.org/W2033669961","https://openalex.org/W2971899271","https://openalex.org/W1972167985","https://openalex.org/W2350644419"],"abstract_inverted_index":{"In":[0,45],"PhysioNet/Computing":[1],"in":[2,103],"Cardiology":[3],"Challenge":[4,39,53,69,118,149],"2021,":[5],"we":[6,55,72],"developed":[7],"an":[8],"ensemble":[9],"model":[10,81,102,115],"by":[11],"combining":[12],"different":[13],"epochs":[14,28],"of":[15,64,120],"ResNet":[16],"to":[17,47,49,60,82],"classify":[18],"cardiac":[19],"abnormalities":[20],"from":[21,84],"12,6,4,3,2":[22],"lead":[23],"electrocardiogram":[24],"(ECG)":[25],"signals,":[26],"where":[27],"are":[29,97],"chosen":[30],"based":[31],"on":[32,35,132,141],"validation":[33],"performance":[34],"China":[36],"Physiological":[37],"Signal":[38],"(CPSC)":[40],"dataset":[41],"and":[42,68,94,111,138],"Georgia":[43],"dataset.":[44],"order":[46],"adapt":[48],"the":[50,62,80,85,104,133,142,148],"specially":[51],"designed":[52,56],"score,":[54],"a":[57,75,89,117],"multi-task":[58],"loss":[59,67,96],"combine":[61],"benefit":[63],"binary":[65],"cross-entropy":[66],"loss.":[70],"Besides,":[71],"also":[73],"integrated":[74],"subsample":[76],"frequency":[77],"feature":[78],"into":[79],"learn":[83],"signals.":[86],"To":[87],"gain":[88],"better":[90],"generalization":[91],"ability,":[92],"mixup":[93],"weighted":[95],"introduced.":[98],"We":[99],"submitted":[100],"our":[101,112],"official":[105],"phase":[106],"with":[107,147],"team":[108],"name":[109],"DataLA_NUS,":[110],"final":[113,143],"selected":[114],"achieved":[116],"score":[119],"0.51,":[121,122,123],"0.50,":[124],"0.52":[125],"(ranked":[126],"8th,":[127,130],"5th,":[128],"6th,":[129],"5th)":[131],"12-lead,":[134],"6-lead,":[135],"4-lead,":[136],"3-lead,":[137],"2-lead":[139],"setting":[140],"hidden":[144],"test":[145],"set":[146],"evaluation":[150],"metric.":[151]},"counts_by_year":[{"year":2022,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
