{"id":"https://openalex.org/W4205183970","doi":"https://doi.org/10.23919/cinc53138.2021.9662693","title":"Semi-Supervised Learning for ECG Classification","display_name":"Semi-Supervised Learning for ECG Classification","publication_year":2021,"publication_date":"2021-09-13","ids":{"openalex":"https://openalex.org/W4205183970","doi":"https://doi.org/10.23919/cinc53138.2021.9662693"},"language":"en","primary_location":{"id":"doi:10.23919/cinc53138.2021.9662693","is_oa":false,"landing_page_url":"https://doi.org/10.23919/cinc53138.2021.9662693","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/A5101839285","display_name":"Rui Rodrigues","orcid":"https://orcid.org/0000-0003-3522-9844"},"institutions":[{"id":"https://openalex.org/I83558840","display_name":"Universidade Nova de Lisboa","ror":"https://ror.org/02xankh89","country_code":"PT","type":"education","lineage":["https://openalex.org/I83558840"]}],"countries":["PT"],"is_corresponding":true,"raw_author_name":"Rui Rodrigues","raw_affiliation_strings":["Center for Mathematics and Applications (CMA), FCT NOVA, Caparica, Portugal","Departamento de Matem\u00e1tica, Faculdade de Ci\u00eancias e Tecnologia, Caparica, Portugal","Department of Mathematics, Caparica, FCT NOVA, Portugal"],"affiliations":[{"raw_affiliation_string":"Center for Mathematics and Applications (CMA), FCT NOVA, Caparica, Portugal","institution_ids":["https://openalex.org/I83558840"]},{"raw_affiliation_string":"Departamento de Matem\u00e1tica, Faculdade de Ci\u00eancias e Tecnologia, Caparica, Portugal","institution_ids":[]},{"raw_affiliation_string":"Department of Mathematics, Caparica, FCT NOVA, Portugal","institution_ids":["https://openalex.org/I83558840"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5039929047","display_name":"Paula Couto","orcid":"https://orcid.org/0000-0002-6181-3422"},"institutions":[{"id":"https://openalex.org/I83558840","display_name":"Universidade Nova de Lisboa","ror":"https://ror.org/02xankh89","country_code":"PT","type":"education","lineage":["https://openalex.org/I83558840"]}],"countries":["PT"],"is_corresponding":false,"raw_author_name":"Paula Couto","raw_affiliation_strings":["Center for Mathematics and Applications (CMA), FCT NOVA, Caparica, Portugal","Department of Mathematics, Caparica, FCT NOVA, Portugal"],"affiliations":[{"raw_affiliation_string":"Center for Mathematics and Applications (CMA), FCT NOVA, Caparica, Portugal","institution_ids":["https://openalex.org/I83558840"]},{"raw_affiliation_string":"Department of Mathematics, Caparica, FCT NOVA, Portugal","institution_ids":["https://openalex.org/I83558840"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5101839285"],"corresponding_institution_ids":["https://openalex.org/I83558840"],"apc_list":null,"apc_paid":null,"fwci":2.761,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.92743363,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"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":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":0.9977999925613403,"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/T10323","display_name":"Analog and Mixed-Signal Circuit Design","score":0.9914000034332275,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.9015620946884155},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6907037496566772},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6534202694892883},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5728735327720642},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5662169456481934},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.4791775941848755},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.46152251958847046},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4161810874938965},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3802057206630707},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.36334437131881714},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.07101839780807495}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.9015620946884155},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6907037496566772},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6534202694892883},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5728735327720642},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5662169456481934},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.4791775941848755},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.46152251958847046},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4161810874938965},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3802057206630707},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.36334437131881714},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.07101839780807495},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/cinc53138.2021.9662693","is_oa":false,"landing_page_url":"https://doi.org/10.23919/cinc53138.2021.9662693","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":[{"id":"https://openalex.org/G1878529617","display_name":null,"funder_award_id":"UIDB/00297/2020","funder_id":"https://openalex.org/F4320334779","funder_display_name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia"}],"funders":[{"id":"https://openalex.org/F4320334779","display_name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","ror":"https://ror.org/00snfqn58"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W2162800060","https://openalex.org/W2888456553","https://openalex.org/W2896457183","https://openalex.org/W2996959172","https://openalex.org/W3008167346","https://openalex.org/W3027572331","https://openalex.org/W3049106197","https://openalex.org/W3098699929","https://openalex.org/W3119282903","https://openalex.org/W4206135956","https://openalex.org/W4385245566","https://openalex.org/W6739901393","https://openalex.org/W6755207826","https://openalex.org/W6774025570","https://openalex.org/W6807079463","https://openalex.org/W6884848120"],"related_works":["https://openalex.org/W1574414179","https://openalex.org/W4362597605","https://openalex.org/W3009056573","https://openalex.org/W2922073769","https://openalex.org/W4297676672","https://openalex.org/W4281702477","https://openalex.org/W4378510483","https://openalex.org/W4376166922","https://openalex.org/W2490526372","https://openalex.org/W3099765033"],"abstract_inverted_index":{"We":[0],"present":[1],"an":[2,32,117,149],"approach":[3,14],"for":[4,228],"automatic":[5],"cardiac":[6],"abnormality":[7],"detection":[8],"using":[9],"two":[10],"leads":[11],"ECG.":[12],"This":[13],"was":[15],"developed":[16],"in":[17,23,109,241],"the":[18,21,51,71,103,107,125,131,139,145,160,168,174,184,194,201,218,236,239,245],"context":[19],"of":[20,56,176,215],"Physionet/Computing":[22],"Cardiology":[24,242],"Challenge":[25,52],"2021.":[26],"Our":[27,207],"model":[28,43,64,203],"is":[29,38,65,76,89,128,142,171],"decomposed":[30],"into":[31],"Encoder":[33,108],"and":[34,199],"a":[35,39,77,90,96,110,212],"Decoder.":[36],"It":[37],"huge":[40],"neural":[41,79,99],"network":[42,80],"with":[44,193],"more":[45,57],"than":[46,58],"36":[47],"million":[48],"parameters.":[49],"Although":[50],"training":[53,72],"dataset":[54],"consists":[55],"88":[59],"thousand":[60],"annotated":[61],"ECGs,":[62],"our":[63],"extremely":[66],"prone":[67],"to":[68,70,129,143,172,226,234,238],"overfitting":[69],"data.":[73],"The":[74,87,187],"encoder":[75,85,92],"convolution":[78],"followed":[81,94],"by":[82,95],"three":[83,114],"transformer":[84,91],"blocks.":[86],"decoder":[88],"block":[93],"feed":[97],"forward":[98],"network.":[100],"To":[101],"reduce":[102],"overfitting,":[104],"we":[105,223,231],"pretrain":[106],"semi-supervised":[111],"way":[112],"on":[113,133,148,163,217],"tasks.":[115],"Given":[116],"ECG":[118,146,150],"segment,":[119,151],"L":[120,134,154],"<inf":[121,135,155],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[122,136,156,165,180],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">1</inf>":[123,137,157],",":[124,158,182],"first":[126],"task":[127,141,170],"detect":[130],"QRS":[132,161],";":[138,167],"second":[140],"predict":[144,173],"shape":[147],"L2":[152],"following":[153],"given":[159],"location":[162],"<tex":[164,179],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">$L_{2}$</tex>":[166],"third":[169],"number":[175],"samples,":[177],"after":[178],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">$L_{1}$</tex>":[181],"before":[183,244],"next":[185],"QRS.":[186],"Decoder":[188],"weights":[189],"were":[190,205,224],"firstly":[191],"estimated":[192],"frozen":[195],"Endoder":[196],"pre-trained":[197],"parameters":[198,204],"then":[200],"whole":[202],"fine-tunned.":[206],"team,":[208],"named":[209],"matFCT,":[210],"received":[211],"challenge":[213],"score":[214],"0.43":[216],"official":[219],"test":[220],"dataset.":[221],"However,":[222],"unable":[225],"qualify":[227],"ranking":[229],"because":[230],"weren't":[232],"able":[233],"submit":[235],"preprint":[237],"Computing":[240],"Conference":[243],"deadline.":[246]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}