{"id":"https://openalex.org/W3089867583","doi":"https://doi.org/10.3906/elk-2004-68","title":"Chronic obstructive pulmonary disease severity analysis using deep learning on multi-channel lung sounds","display_name":"Chronic obstructive pulmonary disease severity analysis using deep learning on multi-channel lung sounds","publication_year":2020,"publication_date":"2020-06-09","ids":{"openalex":"https://openalex.org/W3089867583","doi":"https://doi.org/10.3906/elk-2004-68","mag":"3089867583"},"language":"en","primary_location":{"id":"doi:10.3906/elk-2004-68","is_oa":false,"landing_page_url":"https://doi.org/10.3906/elk-2004-68","pdf_url":null,"source":{"id":"https://openalex.org/S32837994","display_name":"TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES","issn_l":"1300-0632","issn":["1300-0632","1303-6203"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318422","host_organization_name":"Scientific and Technological Research Council of Turkey (TUBITAK)","host_organization_lineage":["https://openalex.org/P4310318422"],"host_organization_lineage_names":["Scientific and Technological Research Council of Turkey (TUBITAK)"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"TURKISH JOURNAL OF ELECTRICAL ENGINEERING &amp; COMPUTER SCIENCES","raw_type":"journal-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/A5071943644","display_name":"G\u00f6khan Altan","orcid":"https://orcid.org/0000-0001-7883-3131"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"G\u00f6khan ALTAN","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062586029","display_name":"Yakup Kutlu","orcid":"https://orcid.org/0000-0002-9853-2878"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yakup KUTLU","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5068715785","display_name":"Ahmet G\u00f6k\u00e7en","orcid":"https://orcid.org/0000-0002-7569-5447"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ahmet G\u00d6K\u00c7EN","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5071943644"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":4.5572,"has_fulltext":false,"cited_by_count":64,"citation_normalized_percentile":{"value":0.96125626,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":"28","issue":"5","first_page":"2979","last_page":"2996"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12419","display_name":"Phonocardiography and Auscultation Techniques","score":0.9986000061035156,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory 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/T12419","display_name":"Phonocardiography and Auscultation Techniques","score":0.9986000061035156,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory 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/T11309","display_name":"Music and Audio Processing","score":0.9718999862670898,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T10143","display_name":"Chronic Obstructive Pulmonary Disease (COPD) Research","score":0.9458000063896179,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/copd","display_name":"COPD","score":0.7908005714416504},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.7447036504745483},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.6833323836326599},{"id":"https://openalex.org/keywords/lung","display_name":"Lung","score":0.5527176856994629},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4888536334037781},{"id":"https://openalex.org/keywords/cardiology","display_name":"Cardiology","score":0.42368435859680176},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.41849613189697266},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.40200531482696533},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.2464732825756073}],"concepts":[{"id":"https://openalex.org/C2776780178","wikidata":"https://www.wikidata.org/wiki/Q199804","display_name":"COPD","level":2,"score":0.7908005714416504},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.7447036504745483},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.6833323836326599},{"id":"https://openalex.org/C2777714996","wikidata":"https://www.wikidata.org/wiki/Q7886","display_name":"Lung","level":2,"score":0.5527176856994629},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4888536334037781},{"id":"https://openalex.org/C164705383","wikidata":"https://www.wikidata.org/wiki/Q10379","display_name":"Cardiology","level":1,"score":0.42368435859680176},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.41849613189697266},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40200531482696533},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.2464732825756073}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3906/elk-2004-68","is_oa":false,"landing_page_url":"https://doi.org/10.3906/elk-2004-68","pdf_url":null,"source":{"id":"https://openalex.org/S32837994","display_name":"TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES","issn_l":"1300-0632","issn":["1300-0632","1303-6203"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318422","host_organization_name":"Scientific and Technological Research Council of Turkey (TUBITAK)","host_organization_lineage":["https://openalex.org/P4310318422"],"host_organization_lineage_names":["Scientific and Technological Research Council of Turkey (TUBITAK)"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"TURKISH JOURNAL OF ELECTRICAL ENGINEERING &amp; COMPUTER SCIENCES","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7599999904632568,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2669956259","https://openalex.org/W2939353110","https://openalex.org/W4327774331","https://openalex.org/W4287178339","https://openalex.org/W3165463024","https://openalex.org/W3165097609","https://openalex.org/W4292874285","https://openalex.org/W4310034804","https://openalex.org/W2732415564","https://openalex.org/W3217300629"],"abstract_inverted_index":{"Chronic":[0],"obstructive":[1],"pulmonary":[2],"disease":[3,70],"(COPD)":[4],"is":[5,140,241],"one":[6,141],"of":[7,38,58,68,142,200,223,236],"the":[8,56,59,66,69,72,92,143,154,179,214],"deadliest":[9],"diseases":[10],"which":[11,139],"cannot":[12],"be":[13,17],"treated":[14],"but":[15],"can":[16],"kept":[18],"under":[19,213],"control":[20],"in":[21,153],"certain":[22],"stages.":[23,36],"COPD":[24,39,60,82,194,237],"has":[25,191],"five":[26,193],"severities,":[27],"including":[28],"at-risk,":[29],"mild,":[30],"moderate,":[31],"severe,":[32],"and":[33,102,120,146,159,168,204,211,230,260],"very":[34],"severe":[35],"Diagnosis":[37],"at":[40,54],"early":[41],"stages":[42],"needs":[43],"additional":[44],"clinical":[45,93],"tests":[46],"for":[47,64,206,234],"even":[48],"experienced":[49],"specialists.":[50],"The":[51,86,183,219],"study":[52,240],"aims":[53],"detecting":[55],"severity":[57],"to":[61,71,112,125,165,264],"start":[62],"treatment":[63],"preventing":[65],"progression":[67],"next":[73],"levels.":[74],"We":[75],"analyzed":[76],"12-channel":[77,224],"lung":[78,87,117,225,232,249],"sounds":[79,88,226],"with":[80,173,188,196],"different":[81],"severities":[83,195],"from":[84,91,96],"RespiratoryDatabase@TR.":[85],"were":[89,123,163],"recorded":[90],"auscultation":[94],"points":[95],"41":[97],"patients":[98],"on":[99,116,129,248],"posterior":[100],"(chest)":[101],"anterior":[103],"(back)":[104],"sides.":[105],"3D":[106],"second-order":[107],"difference":[108],"plot":[109],"was":[110,151],"applied":[111],"extract":[113,126],"characteristic":[114,127],"abnormalities":[115,128],"sounds.":[118,250],"Cuboid":[119],"octant-based":[121],"quantizations":[122],"utilized":[124,152],"chaos":[130],"plot.":[131],"Deep":[132],"extreme":[133],"learning":[134,149],"machines":[135],"classifier":[136],"(deep":[137],"ELM),":[138],"most":[144],"stable":[145],"fast":[147,261],"deep":[148,166,185,221,252],"algorithms,":[150],"classification":[155,197],"stage.":[156],"Novel":[157,251],"HessELM":[158],"LuELM":[160,189],"autoencoder":[161],"kernels":[162,254],"adapted":[164],"ELM":[167,181,186,253],"reached":[169],"higher":[170,258],"generalization":[171,259],"capabilities":[172],"a":[174,228,242,257],"faster":[175],"training":[176,262],"speed":[177],"against":[178],"conventional":[180,265],"autoencoder.":[182],"proposed":[184,220],"model":[187],"autoecoder":[190],"separated":[192],"performance":[198],"rates":[199],"94.31%,":[201],"94.28%,":[202],"98.76%,":[203],"0.9659":[205],"overall":[207],"accuracy,":[208],"weighted-sensitivity,":[209],"weighted-specificity,":[210],"area":[212],"curve":[215],"(AUC)":[216],"value,":[217],"respectively.":[218],"analysis":[222],"provides":[227],"standardized":[229],"entire":[231],"assessment":[233],"identification":[235],"severity.":[238],"Our":[239],"pioneering":[243],"approach":[244],"that":[245],"directly":[246],"focuses":[247],"have":[255],"performed":[256],"compared":[263],"kernels.":[266]},"counts_by_year":[{"year":2026,"cited_by_count":6},{"year":2025,"cited_by_count":12},{"year":2024,"cited_by_count":10},{"year":2023,"cited_by_count":19},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":8},{"year":2020,"cited_by_count":2}],"updated_date":"2026-03-24T08:02:53.985720","created_date":"2025-10-10T00:00:00"}
