{"id":"https://openalex.org/W7117992062","doi":"https://doi.org/10.48550/arxiv.2512.24564","title":"CPR: Causal Physiological Representation Learning for Robust ECG Analysis under Distribution Shifts","display_name":"CPR: Causal Physiological Representation Learning for Robust ECG Analysis under Distribution Shifts","publication_year":2025,"publication_date":"2025-12-31","ids":{"openalex":"https://openalex.org/W7117992062","doi":"https://doi.org/10.48550/arxiv.2512.24564"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2512.24564","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2512.24564","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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2512.24564","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5121061486","display_name":"Shunbo Jia","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Jia, Shunbo","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5064209469","display_name":"Caizhi Liao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liao, Caizhi","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5121061486"],"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.8925999999046326,"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"}},"topics":[{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.8925999999046326,"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/T11021","display_name":"ECG Monitoring and Analysis","score":0.04450000077486038,"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/T12122","display_name":"Physical Unclonable Functions (PUFs) and Hardware Security","score":0.008799999952316284,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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/smoothing","display_name":"Smoothing","score":0.6633999943733215},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5424000024795532},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.48190000653266907},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.45840001106262207},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4187999963760376},{"id":"https://openalex.org/keywords/spurious-relationship","display_name":"Spurious relationship","score":0.41600000858306885},{"id":"https://openalex.org/keywords/causal-inference","display_name":"Causal inference","score":0.40119999647140503},{"id":"https://openalex.org/keywords/invariant","display_name":"Invariant (physics)","score":0.3772999942302704}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7020999789237976},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.6633999943733215},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5914999842643738},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5424000024795532},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5318999886512756},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.48190000653266907},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.45840001106262207},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4187999963760376},{"id":"https://openalex.org/C97256817","wikidata":"https://www.wikidata.org/wiki/Q1462316","display_name":"Spurious relationship","level":2,"score":0.41600000858306885},{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.40119999647140503},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.3772999942302704},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35519999265670776},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3310999870300293},{"id":"https://openalex.org/C118671147","wikidata":"https://www.wikidata.org/wiki/Q578714","display_name":"Quantile","level":2,"score":0.3176000118255615},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.3160000145435333},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.3140000104904175},{"id":"https://openalex.org/C122770356","wikidata":"https://www.wikidata.org/wiki/Q1656753","display_name":"Identifiability","level":2,"score":0.30079999566078186},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.3005000054836273},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.26930001378059387},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.26899999380111694},{"id":"https://openalex.org/C11671645","wikidata":"https://www.wikidata.org/wiki/Q5054567","display_name":"Causal model","level":2,"score":0.2524999976158142},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.25200000405311584}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2512.24564","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2512.24564","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":"doi:10.48550/arxiv.2512.24564","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2512.24564","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":"article"},"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":{"Deep":[0],"learning":[1],"models":[2],"for":[3,57],"Electrocardiogram":[4],"(ECG)":[5],"diagnosis":[6],"have":[7],"achieved":[8],"remarkable":[9],"accuracy":[10],"but":[11,37],"exhibit":[12],"fragility":[13],"against":[14],"adversarial":[15],"perturbations,":[16],"particularly":[17],"Smooth":[18],"Adversarial":[19,32],"Perturbations":[20],"(SAP)":[21],"that":[22,63,94,125,141],"mimic":[23],"biological":[24],"morphology.":[25],"Existing":[26],"defenses":[27],"face":[28],"a":[29,39,101,106,115,122,182],"critical":[30],"dilemma:":[31],"Training":[33],"(AT)":[34],"provides":[35],"robustness":[36,172],"incurs":[38],"prohibitive":[40],"computational":[41],"burden,":[42],"while":[43,176],"certified":[44,171],"methods":[45],"like":[46],"Randomized":[47,174],"Smoothing":[48,162,175],"(RS)":[49],"introduce":[50],"significant":[51],"inference":[52,179],"latency,":[53],"rendering":[54],"them":[55],"impractical":[56],"real-time":[58],"clinical":[59,146,189],"monitoring.":[60],"We":[61],"posit":[62],"this":[64],"vulnerability":[65],"stems":[66],"from":[67,133],"the":[68,170],"models'":[69],"reliance":[70],"on":[71,138],"non-robust":[72],"spurious":[73],"correlations":[74],"rather":[75],"than":[76],"invariant":[77,128],"pathological":[78,129],"features.":[79],"To":[80],"address":[81],"this,":[82],"we":[83],"propose":[84],"Causal":[85,117],"Physiological":[86,102],"Representation":[87],"Learning":[88],"(CPR).":[89],"Unlike":[90],"standard":[91,145],"denoising":[92],"approaches":[93],"operate":[95],"without":[96],"semantic":[97],"constraints,":[98],"CPR":[99,120,142,153,168],"incorporates":[100],"Structural":[103,116],"Prior":[104],"within":[105],"causal":[107],"disentanglement":[108],"framework.":[109],"By":[110],"modeling":[111],"ECG":[112],"generation":[113],"via":[114],"Model":[118],"(SCM),":[119],"enforces":[121],"structural":[123],"intervention":[124],"strictly":[126],"separates":[127],"morphology":[130],"(P-QRS-T":[131],"complex)":[132],"non-causal":[134],"artifacts.":[135],"Empirical":[136],"results":[137],"PTB-XL":[139],"demonstrate":[140],"significantly":[143],"outperforms":[144],"preprocessing":[147],"methods.":[148],"Specifically,":[149],"under":[150],"SAP":[151],"attacks,":[152],"achieves":[154],"an":[155],"F1":[156],"score":[157],"of":[158,173],"0.632,":[159],"surpassing":[160],"Median":[161],"(0.541":[163],"F1)":[164],"by":[165],"9.1%.":[166],"Crucially,":[167],"matches":[169],"maintaining":[177],"single-pass":[178],"efficiency,":[180,187],"offering":[181],"superior":[183],"trade-off":[184],"between":[185],"robustness,":[186],"and":[188],"interpretability.":[190]},"counts_by_year":[],"updated_date":"2026-01-02T23:15:32.796280","created_date":"2026-01-02T00:00:00"}