{"id":"https://openalex.org/W2956236586","doi":"https://doi.org/10.1109/isdfs.2019.8757522","title":"Feature Extraction of ECG Signal by using Deep Feature","display_name":"Feature Extraction of ECG Signal by using Deep Feature","publication_year":2019,"publication_date":"2019-06-01","ids":{"openalex":"https://openalex.org/W2956236586","doi":"https://doi.org/10.1109/isdfs.2019.8757522","mag":"2956236586"},"language":"en","primary_location":{"id":"doi:10.1109/isdfs.2019.8757522","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isdfs.2019.8757522","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 7th International Symposium on Digital Forensics and Security (ISDFS)","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/A5016140593","display_name":"Aykut Di\u0307ker","orcid":"https://orcid.org/0000-0002-1207-8548"},"institutions":[{"id":"https://openalex.org/I41055640","display_name":"Bitlis Eren University","ror":"https://ror.org/00mm4ys28","country_code":"TR","type":"education","lineage":["https://openalex.org/I41055640"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Aykut DIKER","raw_affiliation_strings":["Department of Informatics, Bitlis Eren University, Bitlis, Turkey"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Informatics, Bitlis Eren University, Bitlis, Turkey","institution_ids":["https://openalex.org/I41055640"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5070904845","display_name":"Engin Avc\u0131","orcid":"https://orcid.org/0000-0002-5881-1530"},"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":"Engin AVCI","raw_affiliation_strings":["Department of Software Engineering, Firat University, Elazig, Turkey"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Software Engineering, Firat University, Elazig, Turkey","institution_ids":["https://openalex.org/I143396566"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.0122,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.82661664,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"pp","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11021","display_name":"ECG Monitoring and Analysis","score":0.9908000230789185,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.9868000149726868,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/feature-extraction","display_name":"Feature extraction","score":0.7635620832443237},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7626811861991882},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7019471526145935},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.695919394493103},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5974276065826416},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5851819515228271},{"id":"https://openalex.org/keywords/extreme-learning-machine","display_name":"Extreme learning machine","score":0.5077115297317505},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.4944918155670166},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.34240448474884033},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.10542857646942139}],"concepts":[{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.7635620832443237},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7626811861991882},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7019471526145935},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.695919394493103},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5974276065826416},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5851819515228271},{"id":"https://openalex.org/C2780150128","wikidata":"https://www.wikidata.org/wiki/Q21948731","display_name":"Extreme learning machine","level":3,"score":0.5077115297317505},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.4944918155670166},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.34240448474884033},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10542857646942139},{"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},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isdfs.2019.8757522","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isdfs.2019.8757522","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 7th International Symposium on Digital Forensics and Security (ISDFS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W1887565018","https://openalex.org/W1983921492","https://openalex.org/W1994445307","https://openalex.org/W2004475192","https://openalex.org/W2042494420","https://openalex.org/W2052921213","https://openalex.org/W2111072639","https://openalex.org/W2125654608","https://openalex.org/W2134603844","https://openalex.org/W2162800060","https://openalex.org/W2179624132","https://openalex.org/W2301541953","https://openalex.org/W2326454000","https://openalex.org/W2728130819","https://openalex.org/W2734480387","https://openalex.org/W2779046742","https://openalex.org/W2786532341","https://openalex.org/W2789367970","https://openalex.org/W2793784464","https://openalex.org/W2795302640","https://openalex.org/W2796659423","https://openalex.org/W2801036921","https://openalex.org/W2804196013","https://openalex.org/W2822913430","https://openalex.org/W2899675781","https://openalex.org/W2906479576","https://openalex.org/W2953706584","https://openalex.org/W6639593163","https://openalex.org/W6679935922","https://openalex.org/W6685681863","https://openalex.org/W6697650931","https://openalex.org/W6750097528","https://openalex.org/W6764852949"],"related_works":["https://openalex.org/W2067443264","https://openalex.org/W31566076","https://openalex.org/W4297902562","https://openalex.org/W2741186499","https://openalex.org/W2804652951","https://openalex.org/W2556335056","https://openalex.org/W2002678693","https://openalex.org/W1584764049","https://openalex.org/W2743832667","https://openalex.org/W4386889652"],"abstract_inverted_index":{"The":[0,93],"analysis":[1],"and":[2,36,56,124],"classification":[3,37,105,113],"of":[4,15,38,58,79,103],"Electrocardiogram":[5],"(ECG)":[6],"signals":[7],"have":[8],"become":[9],"very":[10],"important":[11,77],"tool":[12],"to":[13,23,33,53,74,89,100,118],"diagnose":[14],"heart":[16,80],"disorders.":[17],"Computer-aided":[18],"techniques":[19],"are":[20],"generally":[21],"used":[22,99],"classify":[24],"biomedical":[25],"application":[26],"areas.":[27],"In":[28,107],"this":[29,108],"paper,":[30],"we":[31],"aim":[32],"feature":[34,62],"extraction":[35],"ECG":[39,45,59,91],"signals.":[40],"Accordingly,":[41],"an":[42],"open":[43],"access":[44],"database":[46],"in":[47,51],"Physionet":[48],"was":[49,72,87,98,111],"employed":[50],"order":[52],"separate":[54],"normal":[55],"abnormal":[57],"records.":[60,92],"Deep":[61],"approach":[63],"which":[64],"is":[65],"based":[66],"on":[67],"Convolutional":[68],"Neural":[69],"Network":[70],"(CNN)":[71],"applied":[73,88],"taking":[75],"out":[76],"features":[78],"recordings.":[81],"Afterward,":[82],"Extreme":[83],"Learning":[84],"Machine":[85],"(ELM)":[86],"the":[90,101,104],"average":[94],"precision":[95],"value":[96],"metric":[97],"performance":[102],"performed.":[106],"content,":[109],"it":[110],"noticed":[112],"success":[114],"values":[115],"were":[116],"achieved":[117],"accuracy":[119],"%":[120,126],"88.33,":[121],"sensitivity":[122],"%89.47":[123],"specificity":[125],"87.80":[127],"with":[128],"ELM.":[129]},"counts_by_year":[{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
