{"id":"https://openalex.org/W7162642769","doi":"https://doi.org/10.48550/arxiv.2605.27583","title":"Information-theoretic Multimodal Representation Learning for Electrocardiogram Signals","display_name":"Information-theoretic Multimodal Representation Learning for Electrocardiogram Signals","publication_year":2026,"publication_date":"2026-05-26","ids":{"openalex":"https://openalex.org/W7162642769","doi":"https://doi.org/10.48550/arxiv.2605.27583"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.27583","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.27583","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":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.27583","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5011303321","display_name":"Phu X. Nguyen","orcid":"https://orcid.org/0000-0001-8637-2507"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nguyen, Phu X.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137192333","display_name":"Konstantinos Kontras","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kontras, Konstantinos","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137225230","display_name":"Wei Dai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dai, Wei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058452657","display_name":"Huy Phan","orcid":"https://orcid.org/0000-0003-4096-785X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Phan, Huy","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090072986","display_name":"Christos Chatzichristos","orcid":"https://orcid.org/0000-0002-9054-5340"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chatzichristos, Christos","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137269198","display_name":"Paul Pu Liang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liang, Paul Pu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137297896","display_name":"Bert Vandenberk","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vandenberk, Bert","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137197781","display_name":"Maarten De Vos","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"De Vos, Maarten","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"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":0.9810000061988831,"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":0.9810000061988831,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.003599999938160181,"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"}},{"id":"https://openalex.org/T10217","display_name":"Cardiac electrophysiology and arrhythmias","score":0.003100000089034438,"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/robustness","display_name":"Robustness (evolution)","score":0.6043999791145325},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5496000051498413},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4715999960899353},{"id":"https://openalex.org/keywords/ranging","display_name":"Ranging","score":0.3741999864578247},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.37220001220703125},{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.359499990940094}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6819999814033508},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6406999826431274},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6043999791145325},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5496000051498413},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4715999960899353},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44200000166893005},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.39149999618530273},{"id":"https://openalex.org/C115051666","wikidata":"https://www.wikidata.org/wiki/Q6522493","display_name":"Ranging","level":2,"score":0.3741999864578247},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.37220001220703125},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.36250001192092896},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.359499990940094},{"id":"https://openalex.org/C124304363","wikidata":"https://www.wikidata.org/wiki/Q673661","display_name":"Abstraction","level":2,"score":0.34540000557899475},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3398999869823456},{"id":"https://openalex.org/C534262118","wikidata":"https://www.wikidata.org/wiki/Q177719","display_name":"Medical diagnosis","level":2,"score":0.33719998598098755},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.32030001282691956},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.2770000100135803}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.27583","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.27583","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.27583","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.27583","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":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.784996509552002}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Electrocardiograms":[0],"(ECGs)":[1],"are":[2],"widely":[3],"used":[4],"non-invasive":[5],"measurements":[6],"of":[7,42,49],"cardiac":[8],"activity":[9],"and":[10,72,82,115,132,151,189,200],"play":[11],"a":[12,74,99],"central":[13],"role":[14],"in":[15],"clinical":[16,25,32,84,174,208],"diagnosis.":[17],"Recent":[18],"multimodal":[19],"approaches":[20],"align":[21],"ECG":[22,43,65,94,105,170,203],"signals":[23],"with":[24,107,177],"reports":[26,33],"to":[27,36,56,147],"incorporate":[28],"diagnostic":[29,54],"semantics,":[30],"but":[31],"often":[34],"fail":[35],"preserve":[37],"the":[38,168],"rich":[39],"physiological":[40],"structure":[41,81],"waveforms,":[44],"particularly":[45,205],"across":[46,184],"multiple":[47,163],"levels":[48],"abstraction":[50],"ranging":[51],"from":[52,68],"coarse":[53],"categories":[55],"fine-grained":[57,207],"morphology.":[58],"To":[59],"address":[60],"this":[61,88],"limitation,":[62],"we":[63,90],"formulate":[64],"representation":[66],"learning":[67],"an":[69],"information-theoretic":[70],"perspective":[71],"derive":[73],"tractable":[75],"objective":[76],"that":[77,195],"jointly":[78],"preserves":[79],"signal":[80],"integrates":[83],"semantics.":[85],"Based":[86],"on":[87,113,129,135,155],"principle,":[89],"propose":[91],"\\textbf{MERIT}":[92],"(Multimodal":[93],"Representation":[95],"via":[96],"Information":[97],"Theory),":[98],"dual-branch":[100],"pretraining":[101],"framework":[102],"combining":[103],"masked":[104],"modeling":[106],"ECG--text":[108],"contrastive":[109],"alignment.":[110],"Extensive":[111],"experiments":[112],"PTB-XL":[114,130,156],"additional":[116],"benchmarks":[117],"demonstrate":[118,194],"consistent":[119],"improvements":[120],"over":[121],"prior":[122],"methods,":[123],"including":[124,187],"gains":[125],"exceeding":[126],"$3%$":[127],"F1":[128,134,154],"All":[131],"$5%$":[133],"SubClass":[136],"classification.":[137],"In":[138],"zero-shot":[139],"evaluation,":[140],"MERIT":[141,196],"further":[142],"improves":[143,181],"performance":[144],"by":[145],"up":[146],"$":[148,152],"+2.66\\%$":[149],"AUC":[150],"+2.11\\%$":[153],"SubClass,":[157],"while":[158],"also":[159],"demonstrating":[160],"robustness":[161],"under":[162],"distribution-shift":[164],"settings.":[165],"Moreover,":[166],"leveraging":[167],"learned":[169],"representations":[171],"for":[172,206],"ECG-conditioned":[173],"text":[175,182],"generation":[176],"large":[178],"language":[179],"models":[180],"quality":[183],"several":[185],"metrics,":[186],"ROUGE":[188],"METEOR.":[190],"Together,":[191],"these":[192],"results":[193],"learns":[197],"more":[198],"informative":[199],"clinically":[201],"meaningful":[202],"representations,":[204],"applications.":[209]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-29T00:00:00"}