{"id":"https://openalex.org/W4205282810","doi":"https://doi.org/10.23919/cinc53138.2021.9662787","title":"Multi-Label Cardiac Abnormality Classification from Electrocardiogram Using Deep Convolutional Neural Networks","display_name":"Multi-Label Cardiac Abnormality Classification from Electrocardiogram Using Deep Convolutional Neural Networks","publication_year":2021,"publication_date":"2021-09-13","ids":{"openalex":"https://openalex.org/W4205282810","doi":"https://doi.org/10.23919/cinc53138.2021.9662787"},"language":"en","primary_location":{"id":"doi:10.23919/cinc53138.2021.9662787","is_oa":false,"landing_page_url":"https://doi.org/10.23919/cinc53138.2021.9662787","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/A5036659387","display_name":"Nima L. Wickramasinghe","orcid":"https://orcid.org/0000-0001-8133-646X"},"institutions":[{"id":"https://openalex.org/I195740183","display_name":"University of Moratuwa","ror":"https://ror.org/0491f5305","country_code":"LK","type":"education","lineage":["https://openalex.org/I195740183"]}],"countries":["LK"],"is_corresponding":true,"raw_author_name":"Nima L Wickramasinghe","raw_affiliation_strings":["Department of Electronic and Telecommunication Engineering, University of Moratuwa, Sri Lanka"],"affiliations":[{"raw_affiliation_string":"Department of Electronic and Telecommunication Engineering, University of Moratuwa, Sri Lanka","institution_ids":["https://openalex.org/I195740183"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5016429499","display_name":"Mohamed Athif","orcid":"https://orcid.org/0000-0001-9470-0873"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mohamed Athif","raw_affiliation_strings":["Department of Biomedical Engineering, Boston University, Boston, USA"],"affiliations":[{"raw_affiliation_string":"Department of Biomedical Engineering, Boston University, Boston, USA","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5036659387"],"corresponding_institution_ids":["https://openalex.org/I195740183"],"apc_list":null,"apc_paid":null,"fwci":4.8317,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":{"value":0.98230088,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":99},"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/T12419","display_name":"Phonocardiography and Auscultation Techniques","score":0.9918000102043152,"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"}},{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9818000197410583,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.7983837723731995},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7496322393417358},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7323113083839417},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6906947493553162},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6829191446304321},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6621001958847046},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.4653995931148529},{"id":"https://openalex.org/keywords/dropout","display_name":"Dropout (neural networks)","score":0.45435938239097595},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.44747987389564514},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.41546663641929626},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.39577600359916687},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.23839259147644043},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08446139097213745}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.7983837723731995},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7496322393417358},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7323113083839417},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6906947493553162},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6829191446304321},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6621001958847046},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.4653995931148529},{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.45435938239097595},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.44747987389564514},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.41546663641929626},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.39577600359916687},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.23839259147644043},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08446139097213745},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/cinc53138.2021.9662787","is_oa":false,"landing_page_url":"https://doi.org/10.23919/cinc53138.2021.9662787","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":15,"referenced_works":["https://openalex.org/W1936750108","https://openalex.org/W2784316640","https://openalex.org/W2888456553","https://openalex.org/W2996959172","https://openalex.org/W3008167346","https://openalex.org/W3015226328","https://openalex.org/W3027572331","https://openalex.org/W3049106197","https://openalex.org/W3098699929","https://openalex.org/W3099085560","https://openalex.org/W3122226502","https://openalex.org/W4206135956","https://openalex.org/W6774025570","https://openalex.org/W6807079463","https://openalex.org/W6884848120"],"related_works":["https://openalex.org/W4298017035","https://openalex.org/W3110700750","https://openalex.org/W3026913501","https://openalex.org/W2792147139","https://openalex.org/W4226354336","https://openalex.org/W2998675825","https://openalex.org/W3128220493","https://openalex.org/W4318954401","https://openalex.org/W2736804899","https://openalex.org/W2897443685"],"abstract_inverted_index":{"This":[0],"paper":[1],"proposes":[2],"a":[3,61,153],"deep":[4,83],"neural":[5],"network":[6,115],"architecture":[7],"to":[8,59,98,102,142,187,196],"perform":[9],"multi-label":[10],"classification":[11],"of":[12,39,129,134,155,170,204],"26":[13],"cardiac":[14],"abnormalities":[15],"from":[16,73],"12-lead":[17],"and":[18,57,68,89,101,112,119,160,167,180],"reduced":[19],"lead":[20],"ECG":[21,37,66,200],"data.":[22],"The":[23,50,64,150],"model":[24,104,193],"was":[25],"created":[26],"by":[27,54],"team":[28],"“NIMA”":[29],"for":[30,48],"the":[31,74,107,110,139,144,188,197],"PhysioNet/Computing":[32],"in":[33,44],"Cardiology":[34],"Challenge":[35],"2021.":[36],"signals":[38,67,76],"at":[40],"most":[41],"20":[42],"seconds":[43],"length":[45],"were":[46],"used":[47,93,137],"training.":[49],"data":[51,148],"are":[52,77,92,117],"preprocessed":[53,65,75],"normalizing,":[55],"resampling,":[56],"zero-padding":[58],"get":[60],"constant-sized":[62],"array.":[63],"Fast":[69],"Fourier":[70],"Transforms":[71],"obtained":[72],"each":[78,95],"fed":[79],"into":[80],"two":[81,122],"separate":[82],"Convolutional":[84],"Neural":[85],"Networks.":[86],"Spatial":[87],"dropouts":[88],"average":[90],"pooling":[91],"between":[94],"convolutional":[96,108],"layer":[97],"reduce":[99,103],"overfitting":[100],"complexity.":[105],"Following":[106],"layers,":[109],"time":[111],"frequency":[113],"domain":[114],"outputs":[116],"concatenated":[118],"passed":[120],"through":[121],"dense":[123],"layers":[124],"that":[125],"output":[126,140],"an":[127],"array":[128,141],"size":[130],"26.":[131],"A":[132],"threshold":[133],"0.13":[135],"is":[136],"on":[138,175],"determine":[143],"class":[145],"while":[146],"addressing":[147],"imbalance.":[149],"method":[151],"achieved":[152],"score":[154],"0.55,":[156,159],"0.51,":[157],"0.56,":[158],"0.56":[161],"ranking":[162],"2nd,":[163],"5th,":[164],"3rd,":[165],"3rd":[166,168],"out":[169],"39":[171],"officially":[172],"ranked":[173],"teams":[174],"12-lead,":[176],"6-lead,":[177],"4-lead,":[178],"3-lead,":[179],"2-lead":[181],"hidden":[182],"test":[183],"datasets,":[184],"respectively,":[185],"according":[186],"challenge":[189],"evaluation":[190],"metric.":[191],"Our":[192],"performs":[194],"comparable":[195],"12":[198],"Lead":[199],"using":[201],"smaller":[202],"subsets":[203],"leads.":[205]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":8}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}