{"id":"https://openalex.org/W3205202681","doi":"https://doi.org/10.3390/sym13101914","title":"PrismatoidPatNet54: An Accurate ECG Signal Classification Model Using Prismatoid Pattern-Based Learning Architecture","display_name":"PrismatoidPatNet54: An Accurate ECG Signal Classification Model Using Prismatoid Pattern-Based Learning Architecture","publication_year":2021,"publication_date":"2021-10-11","ids":{"openalex":"https://openalex.org/W3205202681","doi":"https://doi.org/10.3390/sym13101914","mag":"3205202681"},"language":"en","primary_location":{"id":"doi:10.3390/sym13101914","is_oa":true,"landing_page_url":"https://doi.org/10.3390/sym13101914","pdf_url":"https://www.mdpi.com/2073-8994/13/10/1914/pdf?version=1634197319","source":{"id":"https://openalex.org/S190787756","display_name":"Symmetry","issn_l":"2073-8994","issn":["2073-8994"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Symmetry","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2073-8994/13/10/1914/pdf?version=1634197319","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5076819631","display_name":"Mehmet Ali Kobat","orcid":"https://orcid.org/0000-0002-2217-2925"},"institutions":[{"id":"https://openalex.org/I143396566","display_name":"F\u0131rat University","ror":"https://ror.org/05teb7b63","country_code":"TR","type":"education","lineage":["https://openalex.org/I143396566"]}],"countries":["TR"],"is_corresponding":true,"raw_author_name":"Mehmet Ali Kobat","raw_affiliation_strings":["Department of Cardiology, Firat University Hospital, Firat University, 23119 Elazig, Turkey"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Cardiology, Firat University Hospital, Firat University, 23119 Elazig, Turkey","institution_ids":["https://openalex.org/I143396566"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001857722","display_name":"\u00d6zkan Karaca","orcid":"https://orcid.org/0000-0003-2896-6934"},"institutions":[{"id":"https://openalex.org/I4210093023","display_name":"Gaziantep Children's Hospital","ror":"https://ror.org/00f4kgf41","country_code":"TR","type":"healthcare","lineage":["https://openalex.org/I4210093023"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Ozkan Karaca","raw_affiliation_strings":["Department of Cardiology, Gaziantep Dr. Ersin Arslan Education Research Hospital, 27010 Gaziantep, Turkey"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Cardiology, Gaziantep Dr. Ersin Arslan Education Research Hospital, 27010 Gaziantep, Turkey","institution_ids":["https://openalex.org/I4210093023"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016221020","display_name":"Prabal Datta Barua","orcid":"https://orcid.org/0000-0001-5117-8333"},"institutions":[{"id":"https://openalex.org/I114017466","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041","country_code":"AU","type":"education","lineage":["https://openalex.org/I114017466"]},{"id":"https://openalex.org/I185523456","display_name":"University of Southern Queensland","ror":"https://ror.org/04sjbnx57","country_code":"AU","type":"education","lineage":["https://openalex.org/I185523456"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Prabal Datta Barua","raw_affiliation_strings":["Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia","School of Management & Enterprise, University of Southern Queensland, Toowoomba, QLD 4350, Australia"],"raw_orcid":"https://orcid.org/0000-0001-5117-8333","affiliations":[{"raw_affiliation_string":"Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia","institution_ids":["https://openalex.org/I114017466"]},{"raw_affiliation_string":"School of Management & Enterprise, University of Southern Queensland, Toowoomba, QLD 4350, Australia","institution_ids":["https://openalex.org/I185523456"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5040772000","display_name":"\u015eeng\u00fcl Do\u011fan","orcid":"https://orcid.org/0000-0001-9677-5684"},"institutions":[{"id":"https://openalex.org/I143396566","display_name":"F\u0131rat University","ror":"https://ror.org/05teb7b63","country_code":"TR","type":"education","lineage":["https://openalex.org/I143396566"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Sengul Dogan","raw_affiliation_strings":["Department of Digital Forensics Engineering, College of Technology, Firat University, 23119 Elazig, Turkey"],"raw_orcid":"https://orcid.org/0000-0001-9677-5684","affiliations":[{"raw_affiliation_string":"Department of Digital Forensics Engineering, College of Technology, Firat University, 23119 Elazig, Turkey","institution_ids":["https://openalex.org/I143396566"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5076819631"],"corresponding_institution_ids":["https://openalex.org/I143396566"],"apc_list":{"value":2000,"currency":"CHF","value_usd":2227},"apc_paid":{"value":2000,"currency":"CHF","value_usd":2227},"fwci":2.192,"has_fulltext":true,"cited_by_count":17,"citation_normalized_percentile":{"value":0.89434878,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":"13","issue":"10","first_page":"1914","last_page":"1914"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11021","display_name":"ECG Monitoring and Analysis","score":0.9998999834060669,"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":0.9998999834060669,"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/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9973000288009644,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11196","display_name":"Non-Invasive Vital Sign Monitoring","score":0.9631999731063843,"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/computer-science","display_name":"Computer science","score":0.7722334265708923},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7680741548538208},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7385604381561279},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.7163379788398743},{"id":"https://openalex.org/keywords/extractor","display_name":"Extractor","score":0.7099871635437012},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6162770390510559},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5711089372634888},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.49392467737197876},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4600508213043213},{"id":"https://openalex.org/keywords/k-nearest-neighbors-algorithm","display_name":"k-nearest neighbors algorithm","score":0.4560245871543884},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.43764933943748474},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3851955533027649},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.07318177819252014}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7722334265708923},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7680741548538208},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7385604381561279},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.7163379788398743},{"id":"https://openalex.org/C117978034","wikidata":"https://www.wikidata.org/wiki/Q5422192","display_name":"Extractor","level":2,"score":0.7099871635437012},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6162770390510559},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5711089372634888},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.49392467737197876},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4600508213043213},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.4560245871543884},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.43764933943748474},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3851955533027649},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.07318177819252014},{"id":"https://openalex.org/C21880701","wikidata":"https://www.wikidata.org/wiki/Q2144042","display_name":"Process engineering","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/sym13101914","is_oa":true,"landing_page_url":"https://doi.org/10.3390/sym13101914","pdf_url":"https://www.mdpi.com/2073-8994/13/10/1914/pdf?version=1634197319","source":{"id":"https://openalex.org/S190787756","display_name":"Symmetry","issn_l":"2073-8994","issn":["2073-8994"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Symmetry","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:f25a106d8f4b4e5c98b9cf87fc444ea0","is_oa":true,"landing_page_url":"https://doaj.org/article/f25a106d8f4b4e5c98b9cf87fc444ea0","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":"Symmetry, Vol 13, Iss 10, p 1914 (2021)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2073-8994/13/10/1914/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/sym13101914","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Symmetry; Volume 13; Issue 10; Pages: 1914","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/sym13101914","is_oa":true,"landing_page_url":"https://doi.org/10.3390/sym13101914","pdf_url":"https://www.mdpi.com/2073-8994/13/10/1914/pdf?version=1634197319","source":{"id":"https://openalex.org/S190787756","display_name":"Symmetry","issn_l":"2073-8994","issn":["2073-8994"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Symmetry","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.7200000286102295}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3205202681.pdf","grobid_xml":"https://content.openalex.org/works/W3205202681.grobid-xml"},"referenced_works_count":50,"referenced_works":["https://openalex.org/W1598808445","https://openalex.org/W1967121574","https://openalex.org/W1973898099","https://openalex.org/W2019922150","https://openalex.org/W2025508891","https://openalex.org/W2026699230","https://openalex.org/W2049826704","https://openalex.org/W2062649041","https://openalex.org/W2065591343","https://openalex.org/W2072401654","https://openalex.org/W2074709057","https://openalex.org/W2075112705","https://openalex.org/W2095409369","https://openalex.org/W2112797878","https://openalex.org/W2123803649","https://openalex.org/W2125927307","https://openalex.org/W2139255154","https://openalex.org/W2144935315","https://openalex.org/W2179014496","https://openalex.org/W2402564818","https://openalex.org/W2432436793","https://openalex.org/W2605056515","https://openalex.org/W2755507768","https://openalex.org/W2767076830","https://openalex.org/W2783741806","https://openalex.org/W2792396409","https://openalex.org/W2889838428","https://openalex.org/W2907615080","https://openalex.org/W2919115771","https://openalex.org/W2947481730","https://openalex.org/W2954214015","https://openalex.org/W2967246721","https://openalex.org/W2996959172","https://openalex.org/W3045600551","https://openalex.org/W3077143157","https://openalex.org/W3083983389","https://openalex.org/W3094699490","https://openalex.org/W3120148682","https://openalex.org/W3129130955","https://openalex.org/W3130580848","https://openalex.org/W3133814784","https://openalex.org/W3155180002","https://openalex.org/W3170087560","https://openalex.org/W3172534957","https://openalex.org/W3177771463","https://openalex.org/W3186353389","https://openalex.org/W3190873402","https://openalex.org/W6680962578","https://openalex.org/W6763077073","https://openalex.org/W6799154945"],"related_works":["https://openalex.org/W2965546495","https://openalex.org/W4389116644","https://openalex.org/W2153315159","https://openalex.org/W3103844505","https://openalex.org/W259157601","https://openalex.org/W1487808658","https://openalex.org/W2945706271","https://openalex.org/W2535808783","https://openalex.org/W2114169842","https://openalex.org/W1968695676"],"abstract_inverted_index":{"Background":[0],"and":[1,11,66,96,118,132,139,167],"objective:":[2],"Arrhythmia":[3],"is":[4,12,91,193],"a":[5,100,109,119],"widely":[6],"seen":[7],"cardiologic":[8],"ailment":[9],"worldwide,":[10],"diagnosed":[13],"using":[14,197],"electrocardiogram":[15],"(ECG)":[16],"signals.":[17],"The":[18,135,184,202],"ECG":[19,75,81],"signals":[20,82],"can":[21,29,48],"be":[22,31,34,49],"translated":[23],"manually":[24],"by":[25,38],"human":[26],"experts,":[27],"but":[28],"also":[30],"scheduled":[32],"to":[33,60,104,126,155,174,195],"carried":[35],"out":[36],"automatically":[37],"some":[39],"agents.":[40],"To":[41],"easily":[42],"diagnose":[43],"arrhythmia,":[44],"an":[45,62,74],"intelligent":[46,63],"assistant":[47],"used.":[50],"Machine":[51],"learning-based":[52],"automatic":[53],"arrhythmia":[54],"detection":[55],"models":[56],"have":[57,72],"been":[58,124,153],"proposed":[59,191,211],"create":[61,105,143],"assistant.":[64],"Materials":[65],"Methods:":[67],"In":[68],"this":[69,94,97],"work,":[70],"we":[71],"used":[73,154,173],"dataset.":[76],"This":[77],"dataset":[78],"contains":[79],"1000":[80],"with":[83,114,180],"17":[84],"categories.":[85],"A":[86,148],"new":[87],"hand-modeled":[88],"learning":[89],"network":[90],"developed":[92],"on":[93],"dataset,":[95],"model":[98,192],"uses":[99],"3D":[101],"shape":[102],"(prismatoid)":[103],"textural":[106],"features.":[107,160],"Moreover,":[108],"tunable":[110],"Q":[111],"wavelet":[112],"transform":[113],"low":[115,131],"oscillatory":[116],"parameters":[117],"statistical":[120,140],"feature":[121,141],"extractor":[122,142],"has":[123,152],"applied":[125],"extract":[127],"features":[128,144,179],"at":[129],"both":[130],"high":[133],"levels.":[134],"suggested":[136],"prismatoid":[137,212],"pattern":[138],"from":[145],"53":[146],"sub-bands.":[147],"neighborhood":[149],"component":[150],"analysis":[151],"choose":[156],"the":[157,176,190,198,207,210],"most":[158],"discriminative":[159],"Two":[161],"classifiers,":[162],"k":[163],"nearest":[164],"neighbor":[165],"(kNN)":[166],"support":[168],"vector":[169],"machine":[170],"(SVM),":[171],"were":[172],"classify":[175],"selected":[177],"top":[178],"10-fold":[181],"cross-validation.":[182],"Results:":[183],"calculated":[185],"best":[186],"accuracy":[187],"rate":[188],"of":[189,209],"equal":[194],"97.30%":[196],"SVM":[199],"classifier.":[200],"Conclusion:":[201],"computed":[203],"results":[204],"clearly":[205],"indicate":[206],"success":[208],"pattern-based":[213],"model.":[214]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":6}],"updated_date":"2026-07-30T17:31:21.811387","created_date":"2021-10-25T00:00:00"}
