{"id":"https://openalex.org/W7133119580","doi":"https://doi.org/10.1007/s44163-026-01052-2","title":"Adaptive multiscale dilated Bi-LSTM for automated ECG anomaly detection and classification","display_name":"Adaptive multiscale dilated Bi-LSTM for automated ECG anomaly detection and classification","publication_year":2026,"publication_date":"2026-03-01","ids":{"openalex":"https://openalex.org/W7133119580","doi":"https://doi.org/10.1007/s44163-026-01052-2"},"language":"en","primary_location":{"id":"doi:10.1007/s44163-026-01052-2","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s44163-026-01052-2","pdf_url":null,"source":{"id":"https://openalex.org/S4210220416","display_name":"Discover Artificial Intelligence","issn_l":"2731-0809","issn":["2731-0809"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Discover Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1007/s44163-026-01052-2","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5127767419","display_name":"V. Neelanaryanan","orcid":null},"institutions":[{"id":"https://openalex.org/I876193797","display_name":"Vellore Institute of Technology University","ror":"https://ror.org/00qzypv28","country_code":"IN","type":"education","lineage":["https://openalex.org/I876193797"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"V. Neelanaryanan","raw_affiliation_strings":["School of Computer Science and Engineering, Vellore Institute of Technology Chennai, Chennai, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Vellore Institute of Technology Chennai, Chennai, India","institution_ids":["https://openalex.org/I876193797"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127776436","display_name":"S. K. Sudarsanam","orcid":null},"institutions":[{"id":"https://openalex.org/I876193797","display_name":"Vellore Institute of Technology University","ror":"https://ror.org/00qzypv28","country_code":"IN","type":"education","lineage":["https://openalex.org/I876193797"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"S. K. Sudarsanam","raw_affiliation_strings":["VIT Business School, Vellore Institute of Technology Chennai, Chennai, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"VIT Business School, Vellore Institute of Technology Chennai, Chennai, India","institution_ids":["https://openalex.org/I876193797"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127756962","display_name":"V. Prakash","orcid":null},"institutions":[{"id":"https://openalex.org/I196829312","display_name":"La Trobe University","ror":"https://ror.org/01rxfrp27","country_code":"AU","type":"education","lineage":["https://openalex.org/I196829312"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"V. Prakash","raw_affiliation_strings":["School of Computing, Engineering and Mathematical Sciences, La Trobe University, Melbourne, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computing, Engineering and Mathematical Sciences, La Trobe University, Melbourne, Australia","institution_ids":["https://openalex.org/I196829312"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127762888","display_name":"C. Naveen","orcid":null},"institutions":[{"id":"https://openalex.org/I196829312","display_name":"La Trobe University","ror":"https://ror.org/01rxfrp27","country_code":"AU","type":"education","lineage":["https://openalex.org/I196829312"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"C. Naveen","raw_affiliation_strings":["School of Computing, Engineering and Mathematical Sciences, La Trobe University, Melbourne, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computing, Engineering and Mathematical Sciences, La Trobe University, Melbourne, Australia","institution_ids":["https://openalex.org/I196829312"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5127764742","display_name":"M. Sivakumar","orcid":null},"institutions":[{"id":"https://openalex.org/I876193797","display_name":"Vellore Institute of Technology University","ror":"https://ror.org/00qzypv28","country_code":"IN","type":"education","lineage":["https://openalex.org/I876193797"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"M. Sivakumar","raw_affiliation_strings":["School of Computer Science and Engineering, Vellore Institute of Technology Chennai, Chennai, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Vellore Institute of Technology Chennai, Chennai, India","institution_ids":["https://openalex.org/I876193797"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5127767419"],"corresponding_institution_ids":["https://openalex.org/I876193797"],"apc_list":{"value":990,"currency":"EUR","value_usd":1067},"apc_paid":{"value":990,"currency":"EUR","value_usd":1067},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.36686771,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"6","issue":"1","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":0.7997000217437744,"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":0.7997000217437744,"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/T11922","display_name":"Cardiovascular Effects of Exercise","score":0.056699998676776886,"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":0.05220000073313713,"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/sudden-cardiac-death","display_name":"Sudden cardiac death","score":0.6103000044822693},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6018999814987183},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.5224999785423279},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.4059999883174896},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3546000123023987},{"id":"https://openalex.org/keywords/electrocardiography","display_name":"Electrocardiography","score":0.34049999713897705},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.3264999985694885}],"concepts":[{"id":"https://openalex.org/C2775935837","wikidata":"https://www.wikidata.org/wiki/Q202837","display_name":"Sudden cardiac death","level":2,"score":0.6103000044822693},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6018999814987183},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.5224999785423279},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.46380001306533813},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4388999938964844},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.4059999883174896},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.3959999978542328},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3709999918937683},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.37070000171661377},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3546000123023987},{"id":"https://openalex.org/C2780040984","wikidata":"https://www.wikidata.org/wiki/Q79785","display_name":"Electrocardiography","level":2,"score":0.34049999713897705},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3264999985694885},{"id":"https://openalex.org/C2993353509","wikidata":"https://www.wikidata.org/wiki/Q411867","display_name":"Sudden death","level":2,"score":0.32260000705718994},{"id":"https://openalex.org/C71635504","wikidata":"https://www.wikidata.org/wiki/Q933954","display_name":"Heart rate variability","level":4,"score":0.3043000102043152},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.299699991941452},{"id":"https://openalex.org/C2778198053","wikidata":"https://www.wikidata.org/wiki/Q181754","display_name":"Heart failure","level":2,"score":0.28369998931884766},{"id":"https://openalex.org/C3020132585","wikidata":"https://www.wikidata.org/wiki/Q2671652","display_name":"Diagnostic accuracy","level":2,"score":0.2833000123500824},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.2644999921321869}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1007/s44163-026-01052-2","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s44163-026-01052-2","pdf_url":null,"source":{"id":"https://openalex.org/S4210220416","display_name":"Discover Artificial Intelligence","issn_l":"2731-0809","issn":["2731-0809"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Discover Artificial Intelligence","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:d51a03498f3340b1b848ea312ed7619f","is_oa":true,"landing_page_url":"https://doaj.org/article/d51a03498f3340b1b848ea312ed7619f","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Discover Artificial Intelligence, Vol 6, Iss 1 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1007/s44163-026-01052-2","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s44163-026-01052-2","pdf_url":null,"source":{"id":"https://openalex.org/S4210220416","display_name":"Discover Artificial Intelligence","issn_l":"2731-0809","issn":["2731-0809"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Discover Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Good health and well-being","score":0.8533685207366943,"id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320330412","display_name":"Scheme for Promotion of Academic and Research Collaboration","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":40,"referenced_works":["https://openalex.org/W2114044187","https://openalex.org/W2752038267","https://openalex.org/W2913789442","https://openalex.org/W3033279801","https://openalex.org/W3040351441","https://openalex.org/W3093592907","https://openalex.org/W3106713760","https://openalex.org/W3111345278","https://openalex.org/W3119636506","https://openalex.org/W3127181174","https://openalex.org/W3127637041","https://openalex.org/W3165351430","https://openalex.org/W3168861255","https://openalex.org/W3178718477","https://openalex.org/W3185043298","https://openalex.org/W3197933652","https://openalex.org/W3211200081","https://openalex.org/W4205399998","https://openalex.org/W4205800832","https://openalex.org/W4205840037","https://openalex.org/W4292672784","https://openalex.org/W4292862525","https://openalex.org/W4297538508","https://openalex.org/W4312931084","https://openalex.org/W4317902659","https://openalex.org/W4322773315","https://openalex.org/W4323520270","https://openalex.org/W4378976915","https://openalex.org/W4380591268","https://openalex.org/W4385192608","https://openalex.org/W4386836424","https://openalex.org/W4387682085","https://openalex.org/W4388878472","https://openalex.org/W4389724710","https://openalex.org/W4392120322","https://openalex.org/W4393858472","https://openalex.org/W4396952797","https://openalex.org/W4401610820","https://openalex.org/W4402915238","https://openalex.org/W4404891500"],"related_works":[],"abstract_inverted_index":{"Physical":[0],"activity":[1],"is":[2,77,105],"very":[3],"strongly":[4],"associated":[5],"with":[6],"cardiovascular":[7,30],"as":[8,10,79],"well":[9],"overall":[11],"health.":[12],"A":[13],"plethora":[14],"of":[15,29,47,53,96,171,179,184,208,213,238],"research":[16],"has":[17,141],"evidenced":[18,203],"a":[19,26,61,121,168,176,205,211,235],"decline":[20],"in":[21,65,224,242],"all-cause":[22],"mortality":[23],"rates":[24],"and":[25,33,87,111,173,188,210,234],"diminished":[27],"prevalence":[28],"ailments,":[31],"malignancies,":[32],"metabolic":[34],"disorders":[35],"among":[36],"individuals":[37],"whose":[38],"lifestyle":[39],"had":[40],"regular":[41],"physical":[42],"exercise.":[43],"In":[44,118],"the":[45,51,182,197],"context":[46],"these":[48],"beneficial":[49],"outcomes,":[50],"phenomenon":[52],"sudden":[54],"cardiac":[55,85],"death":[56],"(SCD)":[57,76],"continues":[58],"to":[59,84,106,114,150,256],"present":[60],"distressing":[62],"reality,":[63],"even":[64],"seemingly":[66],"fit":[67],"athletes":[68],"competing":[69],"at":[70,175,191],"elite":[71],"standards.":[72],"Sudden":[73],"Cardiac":[74],"Death":[75],"characterized":[78],"an":[80],"unforeseen":[81],"demise":[82],"attributable":[83],"causes,":[86],"currently,":[88],"there":[89],"exists":[90],"no":[91],"established":[92],"medical":[93],"protocols":[94],"capable":[95],"averting":[97],"its":[98],"occurrence.":[99],"The":[100,160],"most":[101],"effective":[102],"preventative":[103],"measure":[104],"consistently":[107],"monitor":[108],"vital":[109],"signs":[110],"detect":[112],"irregularities":[113],"facilitate":[115],"prompt":[116],"intervention.":[117],"this":[119],"study,":[120],"new":[122],"Adaptive":[123],"Multiscale":[124],"Dilated":[125],"Bidirectional":[126],"Long":[127],"Short-Term":[128],"Memory":[129],"(Bi-LSTM)":[130],"network":[131,145],"for":[132,200,228,232],"ECG":[133],"Anomaly":[134],"Classification":[135],"using":[136],"PTB-XL":[137],"dataset":[138],"(PhysioNet":[139],"2022)":[140],"been":[142],"developed.":[143],"This":[144],"leverages":[146],"multiscale":[147],"dilated":[148],"convolution":[149],"efficiently":[151],"capture":[152],"long-range":[153],"dependencies":[154],"while":[155],"preserving":[156],"fine-grained":[157],"temporal":[158],"features.":[159],"proposed":[161],"model":[162],"demonstrated":[163],"robust":[164],"learning":[165],"capabilities,":[166],"achieving":[167],"training":[169,187],"accuracy":[170,178],"90%":[172],"stabilizing":[174],"validation":[177],"88%":[180],"through":[181],"implementation":[183],"mixed":[185],"precision":[186,212],"early":[189],"stopping":[190],"40":[192],"epochs.":[193],"Evaluation":[194],"metrics":[195],"highlight":[196],"model\u2019s":[198],"reliability":[199],"clinical":[201],"screening,":[202],"by":[204],"high":[206],"specificity":[207],"92.91%":[209],"77.27%":[214],"(F1":[215],"score:":[216],"0.7379),":[217],"effectively":[218],"minimizing":[219],"false":[220],"positives.":[221],"However,":[222],"discrepancies":[223],"AUC":[225],"scores":[226],"(0.67":[227],"Normal":[229],"vs.":[230],"0.57":[231],"Pathological)":[233],"moderate":[236],"sensitivity":[237],"70.62%":[239],"indicate":[240],"challenges":[241],"distinguishing":[243],"subtle":[244],"abnormalities,":[245],"suggesting":[246],"that":[247],"future":[248],"iterations":[249],"could":[250],"benefit":[251],"from":[252],"targeted":[253],"data":[254],"augmentation":[255],"address":[257],"class":[258],"imbalances.":[259]},"counts_by_year":[],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2026-03-02T00:00:00"}