{"id":"https://openalex.org/W7162341998","doi":"https://doi.org/10.1016/j.jestch.2026.102417","title":"A lightweight and robust parallel CNN\u2013LSTM network with random crop augmentation for single-lead raw ECG arrhythmia classification","display_name":"A lightweight and robust parallel CNN\u2013LSTM network with random crop augmentation for single-lead raw ECG arrhythmia classification","publication_year":2026,"publication_date":"2026-05-26","ids":{"openalex":"https://openalex.org/W7162341998","doi":"https://doi.org/10.1016/j.jestch.2026.102417"},"language":"en","primary_location":{"id":"doi:10.1016/j.jestch.2026.102417","doi":"https://doi.org/10.1016/j.jestch.2026.102417","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.jestch.2026.102417","pdf_url":null,"source":{"id":"https://openalex.org/S2764510487","display_name":"Engineering Science and Technology an International Journal","issn_l":"2215-0986","issn":["2215-0986"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","dongbi-b","jufo-2","norway-1","oarl","poland-100"],"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Engineering Science and Technology, an International Journal","raw_type":"journal-article"},"volume":null,"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1016/j.jestch.2026.102417","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136977159","display_name":"Emir Ali Dinsel","orcid":null,"observed_orcids":[]},"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":true,"raw_author_name":"Emir Ali Dinsel","raw_affiliation_strings":["The Faculty of Computer and Information Sciences, Department of Computer Engineering, Konya Technical University, Konya 42130, Turkey"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Faculty of Computer and Information Sciences, Department of Computer Engineering, Konya Technical University, Konya 42130, Turkey","institution_ids":["https://openalex.org/I4210117254"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008064383","display_name":"Halife Kodaz","orcid":"https://orcid.org/0000-0001-8602-4262","observed_orcids":["https://orcid.org/0000-0001-8602-4262"]},"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":"Halife Kodaz","raw_affiliation_strings":["The Faculty of Computer and Information Sciences, Department of Computer Engineering, Konya Technical University, Konya, Turkey"],"raw_orcid":"https://orcid.org/0000-0001-8602-4262","affiliations":[{"raw_affiliation_string":"The Faculty of Computer and Information Sciences, Department of Computer Engineering, Konya Technical University, Konya, Turkey","institution_ids":["https://openalex.org/I4210117254"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5136977159"],"corresponding_institution_ids":["https://openalex.org/I4210117254"],"apc_list":{"value":1800,"currency":"USD","value_usd":1800},"apc_paid":{"value":1800,"currency":"USD","value_usd":1800},"fwci":5.0691,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.94396375,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":93,"max":96},"biblio":{"volume":"79","issue":null,"first_page":"102417","last_page":"102417"},"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/T11196","display_name":"Non-Invasive Vital Sign Monitoring","score":0.0,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/arrhythmia-classification","display_name":"arrhythmia classification","score":0.8579999804496765},{"id":"https://openalex.org/keywords/single-lead-ecg","display_name":"single-lead ECG","score":0.984000027179718},{"id":"https://openalex.org/keywords/lightweight-deep-learning-models","display_name":"lightweight deep learning","score":0.7229999899864197},{"id":"https://openalex.org/keywords/wearable-ecg-monitoring","display_name":"wearable ECG monitoring","score":0.7919999957084656},{"id":"https://openalex.org/keywords/edge-computing","display_name":"edge computing","score":0.7429999709129333},{"id":"https://openalex.org/keywords/temporal-misalignment","display_name":"temporal misalignment","score":0.7110000252723694},{"id":"https://openalex.org/keywords/noise-robustness","display_name":"noise robustness","score":0.9359999895095825},{"id":"https://openalex.org/keywords/grad-cam","display_name":"Grad-CAM","score":0.6800000071525574},{"id":"https://openalex.org/keywords/mit-bih-arrhythmia-database","display_name":"MIT-BIH arrhythmia database","score":0.8980000019073486},{"id":"https://openalex.org/keywords/real-time-inference","display_name":"real-time inference","score":0.7770000100135803},{"id":"https://openalex.org/keywords/computational-efficiency","display_name":"computational efficiency","score":0.5659999847412109},{"id":"https://openalex.org/keywords/qrs-complex","display_name":"QRS complex","score":0.6019999980926514}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5852000117301941},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4569999873638153},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4406999945640564},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4359000027179718},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.4332999885082245},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.33570000529289246},{"id":"https://openalex.org/C137580998","wikidata":"https://www.wikidata.org/wiki/Q235352","display_name":"Crop","level":2,"score":0.30070000886917114},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.27070000767707825},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.26809999346733093},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.2581000030040741},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.25279998779296875}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1016/j.jestch.2026.102417","doi":"https://doi.org/10.1016/j.jestch.2026.102417","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.jestch.2026.102417","pdf_url":null,"source":{"id":"https://openalex.org/S2764510487","display_name":"Engineering Science and Technology an International Journal","issn_l":"2215-0986","issn":["2215-0986"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","dongbi-b","jufo-2","norway-1","oarl","poland-100"],"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Engineering Science and Technology, an International Journal","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:e9e7326b410a4954b3ce66251405a4e6","doi":null,"is_oa":true,"landing_page_url":"https://doaj.org/article/e9e7326b410a4954b3ce66251405a4e6","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,"listed_in":[],"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":"Engineering Science and Technology, an International Journal, Vol 79, Iss , Pp 102417- (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1016/j.jestch.2026.102417","doi":"https://doi.org/10.1016/j.jestch.2026.102417","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.jestch.2026.102417","pdf_url":null,"source":{"id":"https://openalex.org/S2764510487","display_name":"Engineering Science and Technology an International Journal","issn_l":"2215-0986","issn":["2215-0986"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","dongbi-b","jufo-2","norway-1","oarl","poland-100"],"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Engineering Science and Technology, an International Journal","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3","score":0.6240000128746033}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W2064675550","https://openalex.org/W2095409369","https://openalex.org/W2162800060","https://openalex.org/W2748902594","https://openalex.org/W2794550444","https://openalex.org/W2795340004","https://openalex.org/W2806806521","https://openalex.org/W2902644322","https://openalex.org/W2919115771","https://openalex.org/W2961638199","https://openalex.org/W3013206380","https://openalex.org/W3013966144","https://openalex.org/W3016369805","https://openalex.org/W3039868745","https://openalex.org/W3120992226","https://openalex.org/W3133814784","https://openalex.org/W3207556990","https://openalex.org/W4221130119","https://openalex.org/W4304979269","https://openalex.org/W4308113771","https://openalex.org/W4318054833","https://openalex.org/W4365520047","https://openalex.org/W4390291290","https://openalex.org/W4391517686","https://openalex.org/W4392779950","https://openalex.org/W4392902362","https://openalex.org/W4400614732","https://openalex.org/W4400788632","https://openalex.org/W4402990516","https://openalex.org/W4411466190","https://openalex.org/W4411874143","https://openalex.org/W4414017989","https://openalex.org/W4414042227","https://openalex.org/W4414544126","https://openalex.org/W4415650260","https://openalex.org/W4416386884","https://openalex.org/W7133185032"],"related_works":["https://openalex.org/W7167744726","https://openalex.org/W4392779950","https://openalex.org/W7165660257","https://openalex.org/W7165043067","https://openalex.org/W7127456851","https://openalex.org/W7202366533","https://openalex.org/W7213453003","https://openalex.org/W7162866181","https://openalex.org/W4294789908","https://openalex.org/W3204788325"],"abstract_inverted_index":{"Cardiovascular":[0],"diseases":[1],"remain":[2],"one":[3],"of":[4,8,35,231,293],"the":[5,108,113,227,247,264,275,307,314,337],"leading":[6],"causes":[7],"mortality":[9],"worldwide,":[10],"creating":[11],"a":[12,77,90],"strong":[13],"need":[14],"for":[15,43,82,234,239,306,313,347],"continuous":[16],"and":[17,40,59,112,131,141,151,160,167,170,191,204,216,236,245,256,278,297,310,343],"reliable":[18],"electrocardiogram":[19],"(ECG)":[20],"monitoring":[21,68,355],"systems.":[22],"Although":[23],"recent":[24],"deep":[25],"learning":[26],"methods":[27],"have":[28],"achieved":[29,148],"high":[30],"arrhythmia":[31,86,351],"classification":[32,123,352],"accuracy,":[33],"many":[34],"them":[36],"are":[37],"computationally":[38,344],"demanding":[39],"less":[41],"suitable":[42],"wearable":[44,354],"or":[45],"edge-oriented":[46],"applications.":[47],"In":[48,282],"addition,":[49],"single-lead":[50,84,349],"ECG":[51,85,350],"recordings":[52],"may":[53],"exhibit":[54],"temporal":[55,100],"misalignment,":[56],"segment":[57],"variability,":[58],"noise,":[60],"which":[61],"can":[62],"reduce":[63],"model":[64,265,302],"robustness":[65,98,218],"in":[66,291,330,353],"practical":[67,209,345],"scenarios.":[69,356],"To":[70,206],"address":[71],"these":[72],"challenges,":[73],"this":[74],"study":[75],"proposes":[76],"lightweight":[78],"parallel":[79,136,192],"CNN-LSTM":[80,137],"framework":[81,249,339],"raw":[83],"classification,":[87],"together":[88],"with":[89,189,322],"Random":[91,182],"Crop":[92,183],"augmentation":[93],"strategy":[94],"designed":[95],"to":[96,99,268,284],"improve":[97],"variability.":[101],"The":[102,134,223,300],"proposed":[103,135,248,301,338],"method":[104],"was":[105,201,289],"evaluated":[106],"on":[107,155,164,174,198],"MIT-BIH":[109,127,129,156,165,235,308],"Arrhythmia":[110],"Database":[111,115],"INCART":[114,132,175,240,315],"under":[116,253],"an":[117,341],"intra-patient":[118,348],"5-fold":[119],"cross-validation":[120],"protocol.":[121],"Three":[122],"settings":[124],"were":[125,220],"considered:":[126],"NSVFQ,":[128,157],"NLRAV,":[130,166],"NSV.":[133,176],"consistently":[138],"outperformed":[139],"CNN-only":[140],"LSTM-only":[142,199],"baselines":[143],"across":[144],"all":[145],"tasks.":[146],"It":[147],"99.34%":[149],"accuracy":[150,159,169],"95.59%":[152],"macro":[153,162,172],"F1-score":[154,163,173],"99.58%":[158],"98.63%":[161],"99.74%":[168],"97.22%":[171],"Additional":[177],"ablation":[178],"results":[179,224],"showed":[180,225],"that":[181,226,246,263,336],"generally":[184],"improved":[185],"performance":[186],"when":[187],"combined":[188],"CNN-based":[190],"feature":[193],"learning,":[194],"while":[195,317],"its":[196],"effect":[197],"modeling":[200],"more":[202],"limited":[203],"task-dependent.":[205],"further":[207,261],"assess":[208],"applicability,":[210],"input-length":[211],"sensitivity,":[212],"class-wise":[213],"confusion-matrix":[214],"analysis,":[215],"noise":[217],"experiments":[219],"also":[221],"performed.":[222],"selected":[228],"input":[229],"lengths":[230],"340":[232],"samples":[233,238],"240":[237],"provide":[241],"effective":[242,342],"heartbeat-centered":[243],"context,":[244],"remains":[250],"highly":[251],"stable":[252],"mild-to-moderate":[254],"Gaussian":[255],"EMG-like":[257],"noise.":[258],"Grad-CAM":[259],"visualizations":[260],"indicated":[262],"primarily":[266],"attends":[267],"clinically":[269],"meaningful":[270],"waveform":[271],"regions,":[272],"especially":[273],"around":[274],"QRS":[276],"complex":[277],"class-dependent":[279],"morphological":[280],"transitions.":[281],"addition":[283],"predictive":[285],"performance,":[286],"computational":[287],"efficiency":[288],"analyzed":[290],"terms":[292],"parameter":[294],"count,":[295],"FLOPs,":[296],"inference":[298,320],"latency.":[299],"contains":[303],"2,070,917":[304],"parameters":[305,312],"configuration":[309],"1,865,603":[311],"configuration,":[316],"maintaining":[318],"real-time":[319],"capability":[321],"CPU":[323],"runtime":[324],"below":[325],"1":[326],"ms":[327],"per":[328],"beat":[329],"both":[331],"settings.":[332],"These":[333],"findings":[334],"suggest":[335],"provides":[340],"solution":[346]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-08-21T09:56:20.448147","created_date":"2026-05-26T00:00:00"}