{"id":"https://openalex.org/W7134274641","doi":"https://doi.org/10.17780/ksujes.1852101","title":"ECG SIGNAL CLASSIFICATION WITH DEEP LEARNING AND MACHINE LEARNING: REPRESENTATION OF QRS COMPLEXES IN TWO-DIMENSIONAL FRAMES AND DATA BALANCING WITH SMOTE","display_name":"ECG SIGNAL CLASSIFICATION WITH DEEP LEARNING AND MACHINE LEARNING: REPRESENTATION OF QRS COMPLEXES IN TWO-DIMENSIONAL FRAMES AND DATA BALANCING WITH SMOTE","publication_year":2026,"publication_date":"2026-03-03","ids":{"openalex":"https://openalex.org/W7134274641","doi":"https://doi.org/10.17780/ksujes.1852101"},"language":"tr","primary_location":{"id":"doi:10.17780/ksujes.1852101","doi":"https://doi.org/10.17780/ksujes.1852101","is_oa":true,"landing_page_url":"https://doi.org/10.17780/ksujes.1852101","pdf_url":null,"source":{"id":"https://openalex.org/S4210233714","display_name":"Kahramanmara\u015f S\u00fct\u00e7\u00fc \u0130mam \u00dcniversitesi M\u00fchendislik Bilimleri Dergisi","issn_l":"1309-1751","issn":["1309-1751"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":["tr-dizin"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Kahramanmara\u015f S\u00fct\u00e7\u00fc \u0130mam \u00dcniversitesi M\u00fchendislik Bilimleri Dergisi","raw_type":"journal-article"},"volume":null,"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.17780/ksujes.1852101","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5128114782","display_name":"Mehmet Re\u015fat \u00d6ner","orcid":"https://orcid.org/0000-0003-2084-0181","observed_orcids":["https://orcid.org/0000-0003-2084-0181"]},"institutions":[{"id":"https://openalex.org/I4210117254","display_name":"Konya Technical University","ror":"https://ror.org/02s82rs08","country_code":"TR","type":"education","lineage":["https://openalex.org/I4210117254"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Mehmet Re\u015fat \u00d6ner","raw_affiliation_strings":["KONYA TEKN\u0130K \u00dcN\u0130VERS\u0130TES\u0130"],"raw_orcid":"https://orcid.org/0000-0003-2084-0181","affiliations":[{"raw_affiliation_string":"KONYA TEKN\u0130K \u00dcN\u0130VERS\u0130TES\u0130","institution_ids":["https://openalex.org/I4210117254"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054777079","display_name":"\u0130smail Babao\u011flu","orcid":"https://orcid.org/0000-0002-2503-1482","observed_orcids":["https://orcid.org/0000-0002-2503-1482"]},"institutions":[{"id":"https://openalex.org/I4210117254","display_name":"Konya Technical University","ror":"https://ror.org/02s82rs08","country_code":"TR","type":"education","lineage":["https://openalex.org/I4210117254"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"\u0130smail Babao\u011flu","raw_affiliation_strings":["KONYA TEKN\u0130K \u00dcN\u0130VERS\u0130TES\u0130"],"raw_orcid":"https://orcid.org/0000-0002-2503-1482","affiliations":[{"raw_affiliation_string":"KONYA TEKN\u0130K \u00dcN\u0130VERS\u0130TES\u0130","institution_ids":["https://openalex.org/I4210117254"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5070025689","display_name":"G\u00fclay Tezel","orcid":"https://orcid.org/0000-0003-1698-0106","observed_orcids":["https://orcid.org/0000-0003-1698-0106"]},"institutions":[{"id":"https://openalex.org/I4210117254","display_name":"Konya Technical University","ror":"https://ror.org/02s82rs08","country_code":"TR","type":"education","lineage":["https://openalex.org/I4210117254"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"G\u00fclay Tezel","raw_affiliation_strings":["KONYA TEKN\u0130K \u00dcN\u0130VERS\u0130TES\u0130"],"raw_orcid":"https://orcid.org/0000-0003-1698-0106","affiliations":[{"raw_affiliation_string":"KONYA TEKN\u0130K \u00dcN\u0130VERS\u0130TES\u0130","institution_ids":["https://openalex.org/I4210117254"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210117254"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.12830759,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"29","issue":"1","first_page":"379","last_page":"389"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11021","display_name":"ECG Monitoring and Analysis","score":0.9994999766349792,"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":0.9994999766349792,"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":0.0,"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/T10667","display_name":"Emotion and Mood Recognition","score":0.0,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/ecg-classification","display_name":"ECG classification","score":0.9359999895095825},{"id":"https://openalex.org/keywords/qrs-complex","display_name":"QRS complex","score":0.8820000290870667},{"id":"https://openalex.org/keywords/smote","display_name":"SMOTE","score":0.9150000214576721},{"id":"https://openalex.org/keywords/class-imbalance","display_name":"class imbalance","score":0.9369999766349792},{"id":"https://openalex.org/keywords/convolutional-neural-networks","display_name":"convolutional neural networks","score":0.8740000128746033},{"id":"https://openalex.org/keywords/neural-network","display_name":"artificial neural networks","score":0.9369999766349792},{"id":"https://openalex.org/keywords/k-nearest-neighbors","display_name":"k-nearest neighbors","score":0.9789999723434448},{"id":"https://openalex.org/keywords/mit-bih-arrhythmia-database","display_name":"MIT-BIH arrhythmia database","score":0.9139999747276306},{"id":"https://openalex.org/keywords/computer-aided-diagnosis","display_name":"computer-aided diagnosis","score":0.753000020980835},{"id":"https://openalex.org/keywords/arrhythmia-detection","display_name":"arrhythmia detection","score":0.6890000104904175}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7440999746322632},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7387999892234802},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.635200023651123},{"id":"https://openalex.org/C111773187","wikidata":"https://www.wikidata.org/wiki/Q1969239","display_name":"QRS complex","level":2,"score":0.6187999844551086},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6055999994277954},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.6007000207901001},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5996999740600586},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.4941999912261963},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.47269999980926514},{"id":"https://openalex.org/C116409475","wikidata":"https://www.wikidata.org/wiki/Q1385056","display_name":"External Data Representation","level":2,"score":0.4584999978542328},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4553000032901764},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3698999881744385},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.3490999937057495},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3070000112056732},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.296999990940094},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2678999900817871},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2630999982357025},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.2590000033378601},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.2556000053882599},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.2549000084400177}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.17780/ksujes.1852101","doi":"https://doi.org/10.17780/ksujes.1852101","is_oa":true,"landing_page_url":"https://doi.org/10.17780/ksujes.1852101","pdf_url":null,"source":{"id":"https://openalex.org/S4210233714","display_name":"Kahramanmara\u015f S\u00fct\u00e7\u00fc \u0130mam \u00dcniversitesi M\u00fchendislik Bilimleri Dergisi","issn_l":"1309-1751","issn":["1309-1751"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":["tr-dizin"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Kahramanmara\u015f S\u00fct\u00e7\u00fc \u0130mam \u00dcniversitesi M\u00fchendislik Bilimleri Dergisi","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.17780/ksujes.1852101","doi":"https://doi.org/10.17780/ksujes.1852101","is_oa":true,"landing_page_url":"https://doi.org/10.17780/ksujes.1852101","pdf_url":null,"source":{"id":"https://openalex.org/S4210233714","display_name":"Kahramanmara\u015f S\u00fct\u00e7\u00fc \u0130mam \u00dcniversitesi M\u00fchendislik Bilimleri Dergisi","issn_l":"1309-1751","issn":["1309-1751"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":["tr-dizin"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Kahramanmara\u015f S\u00fct\u00e7\u00fc \u0130mam \u00dcniversitesi M\u00fchendislik Bilimleri Dergisi","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being","score":0.504800021648407}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W2136132422","https://openalex.org/W2148143831","https://openalex.org/W2763350515","https://openalex.org/W2765982206","https://openalex.org/W2780265514","https://openalex.org/W2782620387","https://openalex.org/W3119462501","https://openalex.org/W3138595103","https://openalex.org/W3175617228","https://openalex.org/W3181502416","https://openalex.org/W4205853483","https://openalex.org/W4293483488","https://openalex.org/W4297996402","https://openalex.org/W4378574671","https://openalex.org/W4383901217","https://openalex.org/W4385454058","https://openalex.org/W4387122051"],"related_works":["https://openalex.org/W7130683212","https://openalex.org/W4390829078","https://openalex.org/W4387682085","https://openalex.org/W7144389190","https://openalex.org/W3092508680","https://openalex.org/W7204954750","https://openalex.org/W4411379971","https://openalex.org/W7138892895","https://openalex.org/W6944359704","https://openalex.org/W4296904439"],"abstract_inverted_index":{"This":[0],"study":[1,55],"aims":[2],"to":[3,25,130],"support":[4],"reliable":[5],"ECG":[6,28,38,42,103,139,194],"signal":[7,48],"interpretation":[8],"by":[9],"reducing":[10],"human-dependent":[11],"variability":[12],"through":[13],"computer-aided":[14],"analysis":[15,77],"methods.":[16],"Machine":[17],"learning":[18,21],"and":[19,30,50,64,93,100,134,145,188],"deep":[20],"methods":[22],"were":[23,98,113,148],"employed":[24],"examine":[26],"2D":[27],"representations":[29,140],"Synthetic":[31],"Minority":[32],"Over-Sampling":[33],"Technique":[34],"(SMOTE)-based":[35],"balancing":[36,67,190],"in":[37,115,191],"classification.":[39,120,195],"Unlike":[40],"existing":[41],"classification":[43,83],"studies":[44],"that":[45,159],"typically":[46],"address":[47,122],"representation":[49,63,187],"class":[51,124],"imbalance":[52],"separately,":[53],"this":[54],"jointly":[56],"investigates":[57],"the":[58,109,131,160,164,181],"interaction":[59],"between":[60],"two-dimensional":[61,138,185],"QRS":[62,186],"SMOTE-based":[65],"data":[66,189],"within":[68],"a":[69,75,116,151,172],"unified":[70],"experimental":[71],"framework,":[72],"thereby":[73],"providing":[74],"systematic":[76],"of":[78,108,176,184],"their":[79],"combined":[80,182],"impact":[81],"on":[82,137],"performance.":[84],"Artificial":[85],"Neural":[86,90],"Networks":[87,91],"(ANN),":[88],"Convolutional":[89],"(CNN),":[92],"K-Nearest":[94],"Neighbors":[95],"(KNN)":[96],"algorithms":[97],"implemented":[99],"comparatively":[101],"analyzed.":[102],"beats":[104],"from":[105],"record":[106],"108":[107],"MIT-BIH":[110],"Arrhythmia":[111],"dataset":[112],"represented":[114],"vision-based":[117],"form":[118],"for":[119],"To":[121],"severe":[123],"imbalance,":[125],"SMOTE":[126],"was":[127,141],"applied":[128],"only":[129],"training":[132],"data,":[133],"its":[135],"effect":[136],"explicitly":[142],"examined.":[143],"Normal":[144],"Abnormal":[146],"heartbeats":[147],"classified":[149],"using":[150],"stratified":[152],"5-fold":[153],"cross-validation":[154],"strategy.":[155],"Experimental":[156],"results":[157],"demonstrated":[158],"CNN":[161],"model":[162],"achieved":[163],"most":[165],"successful":[166],"performance":[167],"after":[168],"applying":[169],"SMOTE,":[170],"reaching":[171],"weighted":[173],"average":[174],"F1-score":[175],"99.82%":[177],"\u00b1":[178],"0.002,":[179],"highlighting":[180],"effectiveness":[183],"improving":[192],"automated":[193]},"counts_by_year":[],"updated_date":"2026-09-21T07:43:02.447757","created_date":"2026-03-10T00:00:00"}