{"id":"https://openalex.org/W7171494980","doi":"https://doi.org/10.48550/arxiv.2607.23412","title":"Harmonized Interpretable ECG Waveform Features for Robust Cross-Dataset Clinical Prediction","display_name":"Harmonized Interpretable ECG Waveform Features for Robust Cross-Dataset Clinical Prediction","publication_year":2026,"publication_date":"2026-07-26","ids":{"openalex":"https://openalex.org/W7171494980","doi":"https://doi.org/10.48550/arxiv.2607.23412"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.23412","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.23412","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2607.23412","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5143784343","display_name":"Jie Lin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lin, Jie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5143867350","display_name":"Weijie Sun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Weijie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5143844345","display_name":"Sunil V. Kalmady","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kalmady, Sunil V.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5143806981","display_name":"Anita Khalafbeigi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Khalafbeigi, Anita","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070785370","display_name":"Abram Hindle","orcid":"https://orcid.org/0000-0002-4373-4958"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hindle, Abram","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108147266","display_name":"Padma Kaul","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kaul, Padma","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5143751199","display_name":"Russell Greiner","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Greiner, Russell","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":0.9761999845504761,"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":0.9761999845504761,"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":0.0044999998062849045,"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":0.00279999990016222,"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":0.6238999962806702},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.603600025177002},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.557200014591217},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4855000078678131},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.46709999442100525},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4650000035762787},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.4499000012874603},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.375900000333786}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6323999762535095},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6238999962806702},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.603600025177002},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.557200014591217},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5394999980926514},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4855000078678131},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.46709999442100525},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4650000035762787},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.4499000012874603},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.375900000333786},{"id":"https://openalex.org/C58471807","wikidata":"https://www.wikidata.org/wiki/Q327120","display_name":"Receiver operating characteristic","level":2,"score":0.36559998989105225},{"id":"https://openalex.org/C197424946","wikidata":"https://www.wikidata.org/wiki/Q1165717","display_name":"Waveform","level":3,"score":0.359499990940094},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.34700000286102295},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.34369999170303345},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3434999883174896},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.33379998803138733},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.32280001044273376},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.3082999885082245},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.30720001459121704},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.30000001192092896},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.2953000068664551},{"id":"https://openalex.org/C28427503","wikidata":"https://www.wikidata.org/wiki/Q13580300","display_name":"Internal model","level":3,"score":0.2558000087738037},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2529999911785126},{"id":"https://openalex.org/C2776175482","wikidata":"https://www.wikidata.org/wiki/Q1195816","display_name":"Transfer (computing)","level":2,"score":0.2526000142097473}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.23412","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.23412","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2607.23412","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.23412","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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/3","display_name":"Good health and well-being","score":0.7889471650123596}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":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],"for":[5,205],"cardiovascular":[6],"risk":[7],"prediction,":[8],"yet":[9],"models":[10,85],"often":[11],"fail":[12],"to":[13],"transfer":[14,183],"across":[15],"hospitals":[16],"because":[17],"of":[18,111,121,129],"protocol,":[19],"population,":[20],"and":[21,38,50,79,91,96,117,139,146,168,200],"measurement":[22,57],"differences.":[23],"We":[24,82,100],"benchmark":[25],"cross-dataset":[26,140,182],"generalization":[27],"on":[28,86,162],"three":[29],"tasks":[30],"-":[31,44],"heart":[32],"failure":[33],"classification,":[34],"30-day":[35,39],"all-cause":[36],"mortality,":[37],"mortality":[40],"among":[41],"sinus-rhythm":[42],"ECGs":[43],"using":[45],"two":[46,102],"large":[47],"cohorts":[48],"(MIMIC-IV":[49],"the":[51,122,175],"Alberta":[52],"Cohort).":[53],"To":[54],"reduce":[55],"vendor-specific":[56],"mismatch,":[58],"we":[59],"build":[60],"a":[61,189,202],"harmonized,":[62],"interpretable":[63],"feature":[64,89,124,192],"representation":[65,177],"computed":[66],"directly":[67,161],"from":[68],"raw":[69,163],"waveforms:":[70],"FeatureDB":[71],"morphology/heart-rate-variability":[72],"summaries":[73],"plus":[74],"compact":[75],"time-frequency":[76],"descriptors":[77],"(autoregressive":[78],"wavelet":[80],"features).":[81],"train":[83],"XGBoost":[84],"this":[87],"unified":[88],"space":[90],"evaluate":[92],"with":[93,144,166],"patient-disjoint":[94],"internal":[95,113,119,135,172],"bidirectional":[97],"external":[98,105,198],"testing.":[99],"pre-specify":[101],"hypotheses:":[103],"(H1)":[104],"AUROC":[106,114,120,136,141],"retains":[107],"at":[108],"least":[109],"90%":[110],"source-site":[112],"under":[115,150],"transfer,":[116],"(H2)":[118],"harmonized":[123,176],"set":[125],"stays":[126],"within":[127],"10%":[128],"dataset-native":[130],"machine-measurement":[131],"models.":[132],"Across":[133],"tasks,":[134],"is":[137,142],"0.79-0.82":[138],"0.74-0.78,":[143],"larger":[145],"direction-dependent":[147],"AUPRC":[148],"shifts":[149],"transfer.":[151],"As":[152],"an":[153,156],"exploratory":[154],"benchmark,":[155],"end-to-end":[157],"ConvNeXt":[158],"model":[159],"trained":[160],"ECG":[164],"waveforms":[165],"age":[167],"sex":[169],"achieves":[170],"higher":[171],"AUROC,":[173],"while":[174],"remains":[178],"competitive":[179],"in":[180],"relative":[181],"stability.":[184],"These":[185],"findings":[186],"show":[187],"that":[188],"consistent":[190],"waveform-derived":[191],"interface":[193],"preserves":[194],"performance,":[195],"supports":[196],"realistic":[197],"validation,":[199],"provides":[201],"transparent":[203],"alternative":[204],"cross-site":[206],"clinical":[207],"prediction.":[208]},"counts_by_year":[],"updated_date":"2026-07-29T09:40:50.615796","created_date":"2026-07-29T00:00:00"}