{"id":"https://openalex.org/W4414360216","doi":"https://doi.org/10.24963/ijcai.2025/890","title":"DiffECG: Diffusion Model-Powered Label-Efficient and Personalized Arrhythmia Diagnosis","display_name":"DiffECG: Diffusion Model-Powered Label-Efficient and Personalized Arrhythmia Diagnosis","publication_year":2025,"publication_date":"2025-09-01","ids":{"openalex":"https://openalex.org/W4414360216","doi":"https://doi.org/10.24963/ijcai.2025/890"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2025/890","is_oa":false,"landing_page_url":"https://doi.org/10.24963/ijcai.2025/890","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","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/A5021765802","display_name":"Tianren Zhou","orcid":"https://orcid.org/0000-0001-6630-7791"},"institutions":[{"id":"https://openalex.org/I80143920","display_name":"Shandong University of Science and Technology","ror":"https://ror.org/04gtjhw98","country_code":"CN","type":"education","lineage":["https://openalex.org/I80143920"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Tianren Zhou","raw_affiliation_strings":["School of Computer Science and Technology, Shandong University"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Shandong University","institution_ids":["https://openalex.org/I80143920"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027997476","display_name":"Zhenge Jia","orcid":"https://orcid.org/0000-0002-0554-3608"},"institutions":[{"id":"https://openalex.org/I80143920","display_name":"Shandong University of Science and Technology","ror":"https://ror.org/04gtjhw98","country_code":"CN","type":"education","lineage":["https://openalex.org/I80143920"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhenge Jia","raw_affiliation_strings":["School of Computer Science and Technology, Shandong University"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Shandong University","institution_ids":["https://openalex.org/I80143920"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045982340","display_name":"Dongxiao Yu","orcid":"https://orcid.org/0000-0001-6835-5981"},"institutions":[{"id":"https://openalex.org/I80143920","display_name":"Shandong University of Science and Technology","ror":"https://ror.org/04gtjhw98","country_code":"CN","type":"education","lineage":["https://openalex.org/I80143920"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dongxiao Yu","raw_affiliation_strings":["School of Computer Science and Technology, Shandong University"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Shandong University","institution_ids":["https://openalex.org/I80143920"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5032600760","display_name":"Zhaoyan Shen","orcid":"https://orcid.org/0000-0001-9526-6634"},"institutions":[{"id":"https://openalex.org/I80143920","display_name":"Shandong University of Science and Technology","ror":"https://ror.org/04gtjhw98","country_code":"CN","type":"education","lineage":["https://openalex.org/I80143920"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhaoyan Shen","raw_affiliation_strings":["School of Computer Science and Technology, Shandong University"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Shandong University","institution_ids":["https://openalex.org/I80143920"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5021765802"],"corresponding_institution_ids":["https://openalex.org/I80143920"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.40301601,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"8003","last_page":"8011"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11021","display_name":"ECG Monitoring and Analysis","score":0.8956999778747559,"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":0.8956999778747559,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.599399983882904},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5963000059127808},{"id":"https://openalex.org/keywords/extractor","display_name":"Extractor","score":0.5781000256538391},{"id":"https://openalex.org/keywords/personalization","display_name":"Personalization","score":0.5486999750137329},{"id":"https://openalex.org/keywords/source-code","display_name":"Source code","score":0.4399000108242035},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.43639999628067017},{"id":"https://openalex.org/keywords/diffusion","display_name":"Diffusion","score":0.40149998664855957},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.38089999556541443}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7106999754905701},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.599399983882904},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5963000059127808},{"id":"https://openalex.org/C117978034","wikidata":"https://www.wikidata.org/wiki/Q5422192","display_name":"Extractor","level":2,"score":0.5781000256538391},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5627999901771545},{"id":"https://openalex.org/C183003079","wikidata":"https://www.wikidata.org/wiki/Q1000371","display_name":"Personalization","level":2,"score":0.5486999750137329},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4471000134944916},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.4399000108242035},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.43639999628067017},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.40149998664855957},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.38089999556541443},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36550000309944153},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.35850000381469727},{"id":"https://openalex.org/C89611455","wikidata":"https://www.wikidata.org/wiki/Q6804646","display_name":"Mechanism (biology)","level":2,"score":0.32249999046325684},{"id":"https://openalex.org/C2988455589","wikidata":"https://www.wikidata.org/wiki/Q189331","display_name":"Cardiac arrhythmia","level":3,"score":0.3183000087738037},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.31029999256134033},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.30239999294281006},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.30000001192092896},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2937000095844269},{"id":"https://openalex.org/C148043351","wikidata":"https://www.wikidata.org/wiki/Q4456944","display_name":"Current (fluid)","level":2,"score":0.2840000092983246},{"id":"https://openalex.org/C104122410","wikidata":"https://www.wikidata.org/wiki/Q1416406","display_name":"Network model","level":2,"score":0.27489998936653137},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.27239999175071716},{"id":"https://openalex.org/C32220436","wikidata":"https://www.wikidata.org/wiki/Q2072214","display_name":"Personalized medicine","level":2,"score":0.2621000111103058}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.24963/ijcai.2025/890","is_oa":false,"landing_page_url":"https://doi.org/10.24963/ijcai.2025/890","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","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":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Arrhythmia":[0],"diagnosis":[1],"using":[2],"electrocardiogram":[3],"(ECG)":[4],"is":[5,53,161],"critical":[6],"for":[7,50,69],"preventing":[8],"cardiovascular":[9],"risks.":[10],"However,":[11],"existing":[12],"deep":[13],"learning-based":[14,22],"methods":[15,23],"struggle":[16],"with":[17,87],"label":[18],"scarcity":[19],"and":[20,56,71,92,150,154],"contrastive":[21],"suffer":[24],"from":[25],"false-negative":[26],"samples,":[27],"which":[28],"lead":[29],"to":[30,36,81,98,134],"poor":[31],"model":[32,48,80,108,118,135],"generalization.":[33],"Besides,":[34],"due":[35],"inter-subject":[37],"variability,":[38],"pre-trained":[39],"models":[40],"cannot":[41],"achieve":[42],"evenly":[43],"performance":[44],"across":[45],"individuals.":[46],"Conducting":[47],"fine-tuning":[49],"each":[51],"individual":[52],"computationally":[54],"expensive":[55],"does":[57],"not":[58],"guarantee":[59],"improvement.":[60],"We":[61],"propose":[62,105],"DiffECG,":[63],"a":[64,78,88,93,100],"diffusion-based":[65],"self-supervised":[66],"learning":[67],"framework":[68],"label-efficient":[70],"personalized":[72],"arrhythmia":[73],"detection.":[74],"Our":[75],"method":[76,143,147],"utilizes":[77],"diffusion":[79,127],"extract":[82],"robust":[83],"ECG":[84],"representations,":[85],"coupled":[86],"novel":[89],"feature":[90,95],"extractor":[91],"multi-modal":[94],"fusion":[96],"strategy":[97],"obtain":[99],"well-generalized":[101],"model.":[102],"Moreover,":[103],"we":[104],"an":[106],"efficient":[107],"personalization":[109,155],"mechanism":[110],"based":[111],"on":[112],"zeroth-order":[113],"optimization.":[114],"It":[115],"personalizes":[116],"the":[117,121,126,145],"by":[119,148],"tuning":[120],"noise-adding":[122],"step":[123],"t":[124],"in":[125,152],"process,":[128],"significantly":[129],"reducing":[130],"computational":[131],"costs":[132],"compared":[133],"fine-tuning.":[136],"Experimental":[137],"results":[138],"show":[139],"that":[140],"our":[141],"proposed":[142],"outperforms":[144],"SOTA":[146],"37.9%":[149],"23.9%":[151],"generalization":[153],"performance,":[156],"respectively.":[157],"The":[158],"source":[159],"code":[160],"available":[162],"at:":[163],"https://github.com/Auguuust/DiffEC":[164]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-10-10T00:00:00"}