{"id":"https://openalex.org/W4414969840","doi":"https://doi.org/10.48550/arxiv.2510.03780","title":"A Benchmark Study of Deep Learning Methods for Multi-Label Pediatric Electrocardiogram-Based Cardiovascular Disease Classification","display_name":"A Benchmark Study of Deep Learning Methods for Multi-Label Pediatric Electrocardiogram-Based Cardiovascular Disease Classification","publication_year":2025,"publication_date":"2025-10-04","ids":{"openalex":"https://openalex.org/W4414969840","doi":"https://doi.org/10.48550/arxiv.2510.03780"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2510.03780","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2510.03780","pdf_url":"https://arxiv.org/pdf/2510.03780","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2510.03780","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5063665478","display_name":"Yiqiao Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Chen, Yiqiao","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5063665478"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11021","display_name":"ECG Monitoring and Analysis","score":0.9987000226974487,"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":0.9987000226974487,"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":0.9480000138282776,"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/T11196","display_name":"Non-Invasive Vital Sign Monitoring","score":0.9412000179290771,"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/deep-learning","display_name":"Deep learning","score":0.7669000029563904},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.7037000060081482},{"id":"https://openalex.org/keywords/disease","display_name":"Disease","score":0.4226999878883362},{"id":"https://openalex.org/keywords/hypertrophic-cardiomyopathy","display_name":"Hypertrophic cardiomyopathy","score":0.38449999690055847},{"id":"https://openalex.org/keywords/heart-disease","display_name":"Heart disease","score":0.3416999876499176},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.28949999809265137},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.2888000011444092}],"concepts":[{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7669000029563904},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7037000060081482},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6509000062942505},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.618399977684021},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.5273000001907349},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.46810001134872437},{"id":"https://openalex.org/C2779134260","wikidata":"https://www.wikidata.org/wiki/Q12136","display_name":"Disease","level":2,"score":0.4226999878883362},{"id":"https://openalex.org/C2780185194","wikidata":"https://www.wikidata.org/wiki/Q1364270","display_name":"Hypertrophic cardiomyopathy","level":2,"score":0.38449999690055847},{"id":"https://openalex.org/C2780074459","wikidata":"https://www.wikidata.org/wiki/Q389735","display_name":"Heart disease","level":2,"score":0.3416999876499176},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.28949999809265137},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.2888000011444092},{"id":"https://openalex.org/C19527891","wikidata":"https://www.wikidata.org/wiki/Q1120908","display_name":"Medical physics","level":1,"score":0.2847000062465668},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2782999873161316},{"id":"https://openalex.org/C2780040984","wikidata":"https://www.wikidata.org/wiki/Q79785","display_name":"Electrocardiography","level":2,"score":0.27390000224113464},{"id":"https://openalex.org/C2778797674","wikidata":"https://www.wikidata.org/wiki/Q847583","display_name":"Cardiomyopathy","level":3,"score":0.27219998836517334},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.27070000767707825},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.27059999108314514},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.25279998779296875},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.25060001015663147}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2510.03780","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2510.03780","pdf_url":"https://arxiv.org/pdf/2510.03780","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2510.03780","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2510.03780","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2510.03780","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2510.03780","pdf_url":"https://arxiv.org/pdf/2510.03780","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Cardiovascular":[0],"disease":[1,158],"(CVD)":[2],"is":[3,12,24],"a":[4,19,95],"major":[5],"pediatric":[6,42,163],"health":[7],"burden,":[8],"and":[9,21,66,71,86,105,138,156],"early":[10],"screening":[11],"of":[13,37,97,129],"critical":[14],"importance.":[15],"Electrocardiography":[16],"(ECG),":[17],"as":[18,82,84,119],"noninvasive":[20],"accessible":[22],"tool,":[23],"well":[25],"suited":[26],"for":[27,40,115,150],"this":[28],"purpose.":[29],"This":[30,133],"paper":[31],"presents":[32],"the":[33,46,100,123,127,148],"first":[34],"benchmark":[35,134],"study":[36],"deep":[38],"learning":[39],"multi-label":[41],"CVD":[43,56],"classification":[44],"on":[45,99],"recently":[47],"released":[48],"ZZU-pECG":[49],"dataset,":[50],"comprising":[51],"3716":[52],"recordings":[53],"with":[54,79],"19":[55],"categories.":[57],"We":[58],"systematically":[59],"evaluate":[60],"four":[61],"representative":[62],"paradigms--ResNet-1D,":[63],"BiLSTM,":[64],"Transformer,":[65],"Mamba":[67],"2--under":[68],"both":[69],"9-lead":[70,124],"12-lead":[72,101],"configurations.":[73],"All":[74],"models":[75],"achieved":[76],"strong":[77],"results,":[78],"Hamming":[80],"Loss":[81],"low":[83],"0.0069":[85],"F1-scores":[87],"above":[88],"85%":[89],"in":[90,122],"most":[91],"settings.":[92],"ResNet-1D":[93],"reached":[94],"macro-F1":[96],"94.67%":[98],"subset,":[102,125],"while":[103],"BiLSTM":[104],"Transformer":[106],"also":[107],"showed":[108],"competitive":[109],"performance.":[110],"Per-class":[111],"analysis":[112],"indicated":[113],"challenges":[114],"rare":[116],"conditions":[117],"such":[118],"hypertrophic":[120],"cardiomyopathy":[121],"reflecting":[126],"effect":[128],"limited":[130],"positive":[131],"samples.":[132],"establishes":[135],"reusable":[136],"baselines":[137],"highlights":[139],"complementary":[140],"strengths":[141],"across":[142],"paradigms.":[143],"It":[144],"further":[145],"points":[146],"to":[147,160],"need":[149],"larger-scale,":[151],"multi-center":[152],"validation,":[153],"age-stratified":[154],"analysis,":[155],"broader":[157],"coverage":[159],"support":[161],"real-world":[162],"ECG":[164],"applications.":[165]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-10-09T00:00:00"}