{"id":"https://openalex.org/W7163039291","doi":"https://doi.org/10.48550/arxiv.2605.31249","title":"Learning Cardiac Latent Representations in Vectorcardiogram Space","display_name":"Learning Cardiac Latent Representations in Vectorcardiogram Space","publication_year":2026,"publication_date":"2026-05-29","ids":{"openalex":"https://openalex.org/W7163039291","doi":"https://doi.org/10.48550/arxiv.2605.31249"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.31249","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.31249","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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.2605.31249","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101954757","display_name":"B. Q. Huang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Bosong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137584099","display_name":"Panzhen Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Panzhen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137565075","display_name":"Zengxiang Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Zengxiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137575606","display_name":"Patricia Lee","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lee, Patricia","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137590322","display_name":"Wei Jin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jin, Wei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137597451","display_name":"Alan Wee-Chung Liew","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liew, Alan Wee-Chung","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137595694","display_name":"Ming Jin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jin, Ming","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137541735","display_name":"Shirui Pan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pan, Shirui","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":0.9642999768257141,"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":0.9642999768257141,"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/T10217","display_name":"Cardiac electrophysiology and arrhythmias","score":0.007799999788403511,"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":0.004600000102072954,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/spurious-relationship","display_name":"Spurious relationship","score":0.6444000005722046},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.5595999956130981},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5478000044822693},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5109000205993652},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.4797999858856201},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.3675000071525574},{"id":"https://openalex.org/keywords/external-data-representation","display_name":"External Data Representation","score":0.3614000082015991},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3398999869823456}],"concepts":[{"id":"https://openalex.org/C97256817","wikidata":"https://www.wikidata.org/wiki/Q1462316","display_name":"Spurious relationship","level":2,"score":0.6444000005722046},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6236000061035156},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.5595999956130981},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5554999709129333},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5478000044822693},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5109000205993652},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.4797999858856201},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3675000071525574},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3643999993801117},{"id":"https://openalex.org/C116409475","wikidata":"https://www.wikidata.org/wiki/Q1385056","display_name":"External Data Representation","level":2,"score":0.3614000082015991},{"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/C32848918","wikidata":"https://www.wikidata.org/wiki/Q845789","display_name":"Observable","level":2,"score":0.33739998936653137},{"id":"https://openalex.org/C2780040984","wikidata":"https://www.wikidata.org/wiki/Q79785","display_name":"Electrocardiography","level":2,"score":0.31380000710487366},{"id":"https://openalex.org/C2780486913","wikidata":"https://www.wikidata.org/wiki/Q2512243","display_name":"Vectorcardiography","level":3,"score":0.3077999949455261},{"id":"https://openalex.org/C28006648","wikidata":"https://www.wikidata.org/wiki/Q6934509","display_name":"Multi-task learning","level":3,"score":0.29420000314712524},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.2874999940395355},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.28700000047683716},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.28200000524520874},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.2736999988555908},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.2667999863624573},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.2605000138282776}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.31249","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.31249","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.31249","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.31249","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"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":{"Electrocardiography":[0],"(ECG)":[1],"is":[2],"a":[3,94],"cornerstone":[4],"of":[5,11,47,78,98],"cardiac":[6,51,99],"assessment,":[7],"making":[8],"the":[9,33,40,48,62,86,104,110],"learning":[10,60,93,115,127],"informative":[12],"ECG":[13,35,43,63],"representations":[14,131],"fundamental":[15],"to":[16,22,72,118],"tasks":[17],"ranging":[18],"from":[19,54],"disease":[20],"diagnosis":[21],"clinical":[23],"report":[24],"generation.":[25],"However,":[26],"existing":[27],"methods":[28],"operate":[29,119],"almost":[30],"exclusively":[31],"in":[32,61,103,120,155],"observable":[34],"signal":[36],"space.":[37,106,125],"In":[38],"practice,":[39],"standard":[41],"twelve-lead":[42],"represents":[44],"multiple":[45],"projections":[46],"same":[49],"underlying":[50],"electrical":[52,100],"activity":[53,101],"different":[55],"spatial":[56],"orientations.":[57],"Therefore,":[58],"representation":[59,97,114],"space":[64],"inevitably":[65],"introduces":[66],"substantial":[67],"redundancy,":[68],"which":[69],"may":[70],"lead":[71],"spurious":[73],"correlations":[74],"and":[75,83,139,152],"increased":[76],"risk":[77],"overfitting.":[79],"To":[80],"address":[81],"this":[82,121],"motivated":[84],"by":[85],"Frank":[87],"vectorcardiogram":[88],"(VCG)":[89],"model,":[90],"we":[91],"propose":[92],"unified":[95],"latent":[96,124,129],"directly":[102],"VCG":[105,130,136],"We":[107],"introduce":[108],"LVCG,":[109],"first":[111],"general":[112],"self-supervised":[113],"framework":[116],"designed":[117],"physically":[122],"grounded":[123],"By":[126],"view-invariant":[128],"rather":[132],"than":[133],"lead-specific":[134],"artifacts,":[135],"minimizes":[137],"redundancy":[138],"improves":[140],"generalization.":[141],"LVCG":[142],"generally":[143],"outperforms":[144],"ECG-space":[145],"baselines":[146],"across":[147],"tasks,":[148],"demonstrating":[149],"enhanced":[150],"robustness":[151],"generalization,":[153],"especially":[154],"domain":[156],"shift":[157],"settings.":[158]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-02T00:00:00"}