{"id":"https://openalex.org/W7164389218","doi":"https://doi.org/10.48550/arxiv.2606.12252","title":"Using Explainability as a Training-Time Reliability Signal for Efficient ECG Classification","display_name":"Using Explainability as a Training-Time Reliability Signal for Efficient ECG Classification","publication_year":2026,"publication_date":"2026-06-10","ids":{"openalex":"https://openalex.org/W7164389218","doi":"https://doi.org/10.48550/arxiv.2606.12252"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.12252","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.12252","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":"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.2606.12252","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138433511","display_name":"Veerendhra Kumar Dangeti","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dangeti, Veerendhra Kumar","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138396144","display_name":"Xiao Gu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gu, Xiao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138450017","display_name":"Ying Weng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Weng, Ying","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5138439307","display_name":"Shreyank N Gowda","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gowda, Shreyank N","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":0.8264999985694885,"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":0.8264999985694885,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.04800000041723251,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.03440000116825104,"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/reliability","display_name":"Reliability (semiconductor)","score":0.7628999948501587},{"id":"https://openalex.org/keywords/dropout","display_name":"Dropout (neural networks)","score":0.5672000050544739},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.551800012588501},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5403000116348267},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.4977000057697296},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4821999967098236},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.46799999475479126},{"id":"https://openalex.org/keywords/ambiguity","display_name":"Ambiguity","score":0.44429999589920044}],"concepts":[{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.7628999948501587},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7389000058174133},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5730000138282776},{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.5672000050544739},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.551800012588501},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5410000085830688},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5403000116348267},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.4977000057697296},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4821999967098236},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.46799999475479126},{"id":"https://openalex.org/C2780522230","wikidata":"https://www.wikidata.org/wiki/Q1140419","display_name":"Ambiguity","level":2,"score":0.44429999589920044},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.44119998812675476},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4307999908924103},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.40880000591278076},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4025000035762787},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.39250001311302185},{"id":"https://openalex.org/C13944312","wikidata":"https://www.wikidata.org/wiki/Q7512748","display_name":"Signal-to-noise ratio (imaging)","level":2,"score":0.3675000071525574},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35989999771118164},{"id":"https://openalex.org/C24756922","wikidata":"https://www.wikidata.org/wiki/Q1757694","display_name":"Data quality","level":3,"score":0.3346000015735626},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.3294000029563904},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.3172999918460846},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.27559998631477356},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.262800008058548},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2612000107765198}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.12252","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.12252","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":"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.2606.12252","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.12252","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":"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":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.4758331775665283}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Training":[0],"deep":[1],"neural":[2],"networks":[3],"for":[4,19,93,120,154,190],"clinical":[5,197],"time-series":[6,198],"analysis":[7],"is":[8,27],"computationally":[9],"demanding,":[10],"yet":[11],"many":[12],"healthcare":[13],"settings":[14],"lack":[15],"the":[16],"resources":[17],"required":[18],"repeated":[20],"model":[21,64,131],"development":[22],"and":[23,36,66,107,123,137,164,194],"deployment.":[24],"This":[25],"challenge":[26],"particularly":[28],"evident":[29],"in":[30,171,196],"electrocardiogram":[31],"classification,":[32],"where":[33],"large":[34],"datasets":[35,163],"long":[37],"training":[38,48,91,102,176],"schedules":[39],"make":[40],"efficiency":[41,193],"practically":[42],"important.":[43],"Progressive":[44],"Data":[45],"Dropout":[46],"reduces":[47],"cost":[49],"by":[50,135],"excluding":[51],"samples":[52,69,122],"from":[53],"gradient":[54,155],"updates":[55],"once":[56],"they":[57],"are":[58,71,133,144,152],"learned,":[59],"but":[60],"it":[61],"relies":[62],"on":[63,111],"confidence":[65],"may":[67],"retain":[68],"that":[70,128,181],"difficult":[72],"due":[73],"to":[74,103],"noise":[75],"or":[76],"ambiguity":[77],"rather":[78],"than":[79],"useful":[80],"signal.":[81],"In":[82],"this":[83],"work,":[84],"we":[85,115],"introduce":[86],"ERTS,":[87],"an":[88],"explainability-based":[89],"reliability":[90,195],"signal":[92,189],"efficient":[94],"ECG":[95,162],"classification.":[96],"ERTS":[97,159],"uses":[98],"explanation":[99,182],"quality":[100,183],"during":[101],"distinguish":[104],"between":[105],"informative":[106],"unreliable":[108],"uncertainty.":[109],"Building":[110],"progressive":[112],"data":[113],"selection,":[114],"compute":[116],"Grad-CAM":[117],"attention":[118,151],"maps":[119],"candidate":[121],"derive":[124],"a":[125,187],"focus":[126,143],"score":[127],"measures":[129],"whether":[130],"predictions":[132],"supported":[134],"coherent":[136],"localised":[138],"patterns.":[139],"Samples":[140],"with":[141,149],"low":[142],"filtered":[145],"out,":[146],"while":[147],"those":[148],"meaningful":[150],"prioritised":[153],"updates.":[156],"We":[157],"evaluate":[158],"across":[160],"three":[161],"multiple":[165],"backbone":[166],"architectures,":[167],"showing":[168],"consistent":[169],"improvements":[170],"macro-F1":[172],"alongside":[173],"reduced":[174],"effective":[175],"cost.":[177],"These":[178],"results":[179],"suggest":[180],"can":[184],"serve":[185],"as":[186],"practical":[188],"improving":[191],"both":[192],"learning.":[199],"Code":[200],"will":[201],"be":[202],"released.":[203]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-12T00:00:00"}
