{"id":"https://openalex.org/W7126053204","doi":"https://doi.org/10.1109/bibm66473.2025.11356165","title":"ECG Arrhythmia Classification Using Associative Learning and Caputo-Enhanced Manta Ray Optimization","display_name":"ECG Arrhythmia Classification Using Associative Learning and Caputo-Enhanced Manta Ray Optimization","publication_year":2025,"publication_date":"2025-12-15","ids":{"openalex":"https://openalex.org/W7126053204","doi":"https://doi.org/10.1109/bibm66473.2025.11356165"},"language":null,"primary_location":{"id":"doi:10.1109/bibm66473.2025.11356165","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11356165","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102724092","display_name":"Zhiwei Xiao","orcid":"https://orcid.org/0000-0001-6638-4332"},"institutions":[{"id":"https://openalex.org/I4210165606","display_name":"Hubei Normal University","ror":"https://ror.org/056y3dw16","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210165606"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhiwei Xiao","raw_affiliation_strings":["School of Artificial Intelligence and Computer, Hubei Normal University,Huangshi,China"],"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence and Computer, Hubei Normal University,Huangshi,China","institution_ids":["https://openalex.org/I4210165606"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088808666","display_name":"Jiejie Chen","orcid":"https://orcid.org/0000-0002-7632-4672"},"institutions":[{"id":"https://openalex.org/I4210165606","display_name":"Hubei Normal University","ror":"https://ror.org/056y3dw16","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210165606"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiejie Chen","raw_affiliation_strings":["School of Artificial Intelligence and Computer, Hubei Normal University,Huangshi,China"],"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence and Computer, Hubei Normal University,Huangshi,China","institution_ids":["https://openalex.org/I4210165606"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086523198","display_name":"Bin Wei","orcid":"https://orcid.org/0000-0003-0856-7813"},"institutions":[{"id":"https://openalex.org/I4210165606","display_name":"Hubei Normal University","ror":"https://ror.org/056y3dw16","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210165606"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bin Wei","raw_affiliation_strings":["School of Artificial Intelligence and Computer, Hubei Normal University,Huangshi,China"],"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence and Computer, Hubei Normal University,Huangshi,China","institution_ids":["https://openalex.org/I4210165606"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124287129","display_name":"Xuewen Zhou","orcid":null},"institutions":[{"id":"https://openalex.org/I4210165606","display_name":"Hubei Normal University","ror":"https://ror.org/056y3dw16","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210165606"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuewen Zhou","raw_affiliation_strings":["School of Artificial Intelligence and Computer, Hubei Normal University,Huangshi,China"],"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence and Computer, Hubei Normal University,Huangshi,China","institution_ids":["https://openalex.org/I4210165606"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100708914","display_name":"Xinrui Zhang","orcid":"https://orcid.org/0009-0000-4722-8350"},"institutions":[{"id":"https://openalex.org/I4210165606","display_name":"Hubei Normal University","ror":"https://ror.org/056y3dw16","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210165606"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinrui Zhang","raw_affiliation_strings":["School of Artificial Intelligence and Computer, Hubei Normal University,Huangshi,China"],"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence and Computer, Hubei Normal University,Huangshi,China","institution_ids":["https://openalex.org/I4210165606"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5124167699","display_name":"Ping Jiang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210122157","display_name":"Hubei Polytechnic University","ror":"https://ror.org/01z07eq06","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210122157"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ping Jiang","raw_affiliation_strings":["School of Computer, Hubei PolyTechnic University,Huangshi,China"],"affiliations":[{"raw_affiliation_string":"School of Computer, Hubei PolyTechnic University,Huangshi,China","institution_ids":["https://openalex.org/I4210122157"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5102724092"],"corresponding_institution_ids":["https://openalex.org/I4210165606"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.72077278,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1885","last_page":"1890"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11021","display_name":"ECG Monitoring and Analysis","score":0.984000027179718,"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":0.984000027179718,"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/T10065","display_name":"Atrial Fibrillation Management and Outcomes","score":0.00419999985024333,"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":0.003700000001117587,"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/associative-property","display_name":"Associative property","score":0.6269000172615051},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5242999792098999},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5162000060081482},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4763000011444092},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4607999920845032},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.46000000834465027},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4514000117778778},{"id":"https://openalex.org/keywords/waveform","display_name":"Waveform","score":0.4336000084877014}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7031000256538391},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.694100022315979},{"id":"https://openalex.org/C159423971","wikidata":"https://www.wikidata.org/wiki/Q177251","display_name":"Associative property","level":2,"score":0.6269000172615051},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5242999792098999},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5162000060081482},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4763000011444092},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4607999920845032},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.46000000834465027},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4514000117778778},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44679999351501465},{"id":"https://openalex.org/C197424946","wikidata":"https://www.wikidata.org/wiki/Q1165717","display_name":"Waveform","level":3,"score":0.4336000084877014},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4196000099182129},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4180999994277954},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.40470001101493835},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.36980000138282776},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.36500000953674316},{"id":"https://openalex.org/C116409475","wikidata":"https://www.wikidata.org/wiki/Q1385056","display_name":"External Data Representation","level":2,"score":0.3240000009536743},{"id":"https://openalex.org/C2988455589","wikidata":"https://www.wikidata.org/wiki/Q189331","display_name":"Cardiac arrhythmia","level":3,"score":0.26179999113082886},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.25679999589920044},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.2565999925136566}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm66473.2025.11356165","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11356165","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W2912733464","https://openalex.org/W2982453621","https://openalex.org/W4200105493","https://openalex.org/W4306321664","https://openalex.org/W4399564598","https://openalex.org/W4399599255","https://openalex.org/W4401487456","https://openalex.org/W4405534960","https://openalex.org/W4406559551","https://openalex.org/W4409985180","https://openalex.org/W4410390641","https://openalex.org/W4411874286","https://openalex.org/W4414057521"],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"classification":[1,44],"of":[2],"Electrocardiogram":[3],"(ECG)":[4],"signals":[5],"is":[6],"critical":[7],"for":[8,102,115,143],"arrhythmia":[9,113],"diagnosis,":[10],"yet":[11],"remains":[12],"challenging":[13],"due":[14],"to":[15,95],"complex":[16],"morphological":[17],"variations":[18],"and":[19,31,67],"noise.":[20],"To":[21],"address":[22],"this,":[23],"the":[24,111],"paper":[25],"proposes":[26],"a":[27,68],"novel":[28],"Associative":[29],"Learning":[30],"Non-causal":[32],"Caputo-enhanced":[33],"Manta":[34],"Ray":[35],"Foraging":[36],"Optimization":[37],"(ANCMRFO)":[38],"algorithm":[39],"that":[40,54,73],"significantly":[41],"improves":[42],"ECG":[43,62,79,103,145],"performance.":[45],"The":[46,82,118],"key":[47],"innovations":[48],"include":[49],"an":[50,140],"associative":[51],"learning":[52],"strategy":[53],"enhances":[55],"feature":[56],"representation":[57],"by":[58],"modeling":[59],"interdependencies":[60],"between":[61],"waveform":[63],"segments":[64],"during":[65],"optimization,":[66],"non-causal":[69],"fractional-order":[70],"derivative":[71],"mechanism":[72],"effectively":[74],"captures":[75],"long-range":[76],"dependencies":[77],"in":[78,124,147],"temporal":[80],"patterns.":[81],"algorithm's":[83],"superior":[84],"convergence":[85],"properties":[86],"are":[87],"rigorously":[88],"validated":[89],"on":[90,110],"CEC2022":[91],"benchmark":[92],"functions.":[93],"Applied":[94],"optimize":[96],"Temporal":[97],"Convolutional":[98],"Network":[99],"(TCN)":[100],"architectures":[101],"classification,":[104],"ANCMRFO":[105,138],"achieves":[106],"97.3%":[107],"balanced":[108],"accuracy":[109],"MIT-BIH":[112],"dataset":[114],"five-class":[116],"classification.":[117],"optimized":[119],"model":[120],"demonstrates":[121],"particular":[122],"strength":[123],"distinguishing":[125],"subtle":[126],"arrhythmia,":[127],"while":[128],"maintaining":[129],"robust":[130],"performance":[131],"under":[132],"noisy":[133],"conditions.":[134],"These":[135],"results":[136],"establish":[137],"as":[139],"effective":[141],"solution":[142],"intelligent":[144],"analysis":[146],"clinical":[148],"applications.":[149]},"counts_by_year":[],"updated_date":"2026-02-01T03:34:12.195049","created_date":"2026-01-30T00:00:00"}
