{"id":"https://openalex.org/W7130561237","doi":"https://doi.org/10.5526/err-00042819","title":"Deep learning approaches for ECG-based detection and diagnosis of coronary artery disease","display_name":"Deep learning approaches for ECG-based detection and diagnosis of coronary artery disease","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7130561237","doi":"https://doi.org/10.5526/err-00042819"},"language":"en","primary_location":{"id":"pmh:oai:repository.essex.ac.uk:42819","doi":null,"is_oa":false,"landing_page_url":"https://doi.org/10.5526/ERR-00042819","pdf_url":null,"source":{"id":"https://openalex.org/S4306401236","display_name":"Open Access at Essex (University of Essex)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I110002522","host_organization_name":"University of Essex","host_organization_lineage":["https://openalex.org/I110002522"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"Thesis"},"volume":null,"type":"dissertation","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.5526/err-00042819","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5099566161","display_name":"Atitaya Phoemsuk","orcid":"https://orcid.org/0009-0000-8284-110X","observed_orcids":["https://orcid.org/0009-0000-8284-110X"]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Phoemsuk, Atitaya","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5099566161"],"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":1.0,"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"}},"topics":[{"id":"https://openalex.org/T11021","display_name":"ECG Monitoring and Analysis","score":1.0,"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/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.0,"subfield":{"id":"https://openalex.org/subfields/3605","display_name":"Health Information Management"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"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/coronary-artery-disease","display_name":"coronary artery disease","score":0.9750000238418579},{"id":"https://openalex.org/keywords/electrocardiographic-diagnosis","display_name":"electrocardiographic diagnosis","score":0.8619999885559082},{"id":"https://openalex.org/keywords/automatic-ecg-analysis","display_name":"automated ECG analysis","score":0.6499999761581421},{"id":"https://openalex.org/keywords/1d-convolutional-neural-network","display_name":"1D convolutional neural network","score":0.8880000114440918},{"id":"https://openalex.org/keywords/lightweight-neural-networks","display_name":"lightweight neural networks","score":0.796999990940094},{"id":"https://openalex.org/keywords/12-lead-electrocardiogram","display_name":"12-lead ECG","score":0.8659999966621399},{"id":"https://openalex.org/keywords/squeeze-and-excitation","display_name":"squeeze-and-excitation","score":0.8460000157356262},{"id":"https://openalex.org/keywords/attention-models","display_name":"attention-based models","score":0.5550000071525574},{"id":"https://openalex.org/keywords/prescreening","display_name":"prescreening","score":0.7360000014305115}],"concepts":[{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7795000076293945},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7188000082969666},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6402000188827515},{"id":"https://openalex.org/C194789388","wikidata":"https://www.wikidata.org/wiki/Q17855283","display_name":"CAD","level":2,"score":0.6312000155448914},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5665000081062317},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5192000269889832},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.4763999879360199},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.46889999508857727},{"id":"https://openalex.org/C2778213512","wikidata":"https://www.wikidata.org/wiki/Q844935","display_name":"Coronary artery disease","level":2,"score":0.454800009727478},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.39899998903274536},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3718999922275543},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.35850000381469727},{"id":"https://openalex.org/C66696666","wikidata":"https://www.wikidata.org/wiki/Q17105612","display_name":"Sample entropy","level":3,"score":0.35760000348091125},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.3573000133037567},{"id":"https://openalex.org/C2780040984","wikidata":"https://www.wikidata.org/wiki/Q79785","display_name":"Electrocardiography","level":2,"score":0.35409998893737793},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3391000032424927},{"id":"https://openalex.org/C534262118","wikidata":"https://www.wikidata.org/wiki/Q177719","display_name":"Medical diagnosis","level":2,"score":0.33239999413490295},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2599000036716461},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.2524999976158142}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:repository.essex.ac.uk:42819","doi":null,"is_oa":false,"landing_page_url":"https://doi.org/10.5526/ERR-00042819","pdf_url":null,"source":{"id":"https://openalex.org/S4306401236","display_name":"Open Access at Essex (University of Essex)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I110002522","host_organization_name":"University of Essex","host_organization_lineage":["https://openalex.org/I110002522"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"Thesis"},{"id":"doi:10.5526/err-00042819","doi":"https://doi.org/10.5526/err-00042819","is_oa":true,"landing_page_url":"https://doi.org/10.5526/err-00042819","pdf_url":null,"source":{"id":"https://openalex.org/S7407050750","display_name":"University of Essex","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I110002522","host_organization_name":"University of Essex","host_organization_lineage":["https://openalex.org/I110002522"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Dissertation"}],"best_oa_location":{"id":"doi:10.5526/err-00042819","doi":"https://doi.org/10.5526/err-00042819","is_oa":true,"landing_page_url":"https://doi.org/10.5526/err-00042819","pdf_url":null,"source":{"id":"https://openalex.org/S7407050750","display_name":"University of Essex","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I110002522","host_organization_name":"University of Essex","host_organization_lineage":["https://openalex.org/I110002522"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Dissertation"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","score":0.9391000270843506,"display_name":"Good health and well-being"}],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Coronary":[0],"artery":[1],"disease":[2],"(CAD)":[3],"is":[4,16,31,55],"among":[5],"the":[6,24,105,240,305,308],"most":[7],"prevalent":[8],"and":[9,22,47,63,95,150,174,181,200,222,225,287,294],"life-threatening":[10],"cardiovascular":[11,244],"conditions":[12],"worldwide.":[13],"Early":[14],"detection":[15,241],"essential":[17],"for":[18,34,60,98,239,276],"improving":[19,23,178],"patient":[20],"outcomes":[21],"efficiency":[25],"of":[26,41,57,242,310],"healthcare":[27],"systems.":[28],"Electrocardiography":[29],"(ECG)":[30],"widely":[32],"used":[33,322],"assessing":[35],"cardiac":[36],"function,":[37],"yet":[38],"manual":[39,142],"interpretation":[40],"ECG":[42,79,113,139,157,249,285],"signals":[43,140],"can":[44,267],"be":[45,321],"inconsistent":[46],"prone":[48],"to":[49,109,133,166,284,320],"error.":[50],"Developing":[51],"reliable":[52,269],"automated":[53],"methods":[54,83,319],"therefore":[56],"great":[58],"importance":[59],"enabling":[61,316],"earlier":[62],"more":[64],"consistent":[65,289],"CAD":[66,110,135,271],"detection.":[67],"Although":[68],"recent":[69],"advances":[70],"in":[71,78,86,111,121,258,301,315],"deep":[72,116,264,312],"learning":[73,117,265,313],"have":[74],"achieved":[75,147,226],"strong":[76,152],"performance":[77,290],"analysis,":[80],"many":[81],"existing":[82,193],"remain":[84],"limited":[85,106],"practice.":[87],"Current":[88],"state-of-the-art":[89],"models":[90,118,195,266],"are":[91,282],"often":[92],"computationally":[93],"complex":[94],"thus":[96],"unsuitable":[97],"deployment":[99,228],"on":[100,210,229],"resource-constrained":[101,302],"platforms.":[102],"To":[103],"address":[104],"attention":[107],"given":[108],"current":[112],"research,":[114],"various":[115],"were":[119],"developed":[120],"this":[122,259],"thesis.":[123],"First,":[124],"a":[125,160,184,214,247],"one-dimensional":[126],"convolutional":[127],"neural":[128,186],"network":[129,187],"(1D-CNN)":[130],"was":[131,164,190,237],"proposed":[132,238,280],"detect":[134],"directly":[136],"from":[137,246],"raw":[138],"without":[141,203],"feature":[143,161],"extraction.":[144],"The":[145,206,279],"model":[146,217,236],"97.3%":[148],"accuracy":[149,180],"demonstrated":[151],"generalisability":[153],"when":[154],"using":[155,171],"250-point":[156],"segments.":[158],"Then,":[159],"engineering":[162],"approach":[163],"applied":[165],"select":[167],"high-quality":[168],"signal":[169],"segments":[170],"sample":[172],"entropy":[173],"normalisation":[175],"techniques,":[176],"further":[177],"both":[179,220],"robustness.":[182],"Next,":[183],"lightweight":[185,194,263,311],"architecture":[188],"(CADNet)":[189],"developed,":[191],"outperforming":[192],"by":[196],"offering":[197],"lower":[198],"complexity":[199],"smaller":[201],"size":[202],"compromising":[204],"accuracy.":[205],"fourth":[207],"study":[208,260],"focused":[209],"12-lead":[211],"ECG,":[212],"introducing":[213],"depthwise,":[215],"squeeze-and-excitation-based":[216],"that":[218,262],"captured":[219],"lead-specific":[221],"inter-lead":[223],"patterns,":[224],"efficient":[227],"an":[230,234],"STM32":[231],"microcontroller.":[232],"Finally,":[233],"attention-driven":[235],"multiple":[243],"diseases":[245],"single":[248],"recording,":[250],"demonstrating":[251],"high":[252],"diagnostic":[253,295,318],"capability.":[254],"Our":[255],"qualitative":[256],"evaluations":[257],"demonstrate":[261],"provide":[268],"ECG-based":[270,317],"diagnosis":[272],"while":[273],"remaining":[274],"suitable":[275],"real-time":[277],"deployment.":[278],"approaches":[281],"robust":[283],"variability":[286],"show":[288],"across":[291],"different":[292],"experimental":[293],"scenarios,":[296],"supporting":[297],"their":[298],"practical":[299],"use":[300],"environments.":[303],"Overall,":[304],"results":[306],"highlight":[307],"relevance":[309],"architectures":[314],"as":[323],"potential":[324],"pre-screening":[325],"tools.":[326]},"counts_by_year":[],"updated_date":"2026-09-21T07:43:02.447757","created_date":"2026-02-20T00:00:00"}
