{"id":"https://openalex.org/W4394808256","doi":"https://doi.org/10.1186/s13634-024-01148-w","title":"Performance evaluation of lung sounds classification using deep learning under variable parameters","display_name":"Performance evaluation of lung sounds classification using deep learning under variable parameters","publication_year":2024,"publication_date":"2024-04-15","ids":{"openalex":"https://openalex.org/W4394808256","doi":"https://doi.org/10.1186/s13634-024-01148-w"},"language":"en","primary_location":{"id":"doi:10.1186/s13634-024-01148-w","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s13634-024-01148-w","pdf_url":"https://asp-eurasipjournals.springeropen.com/counter/pdf/10.1186/s13634-024-01148-w","source":{"id":"https://openalex.org/S35920007","display_name":"EURASIP Journal on Advances in Signal Processing","issn_l":"1687-6172","issn":["1687-6172","1687-6180"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"EURASIP Journal on Advances in Signal Processing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://asp-eurasipjournals.springeropen.com/counter/pdf/10.1186/s13634-024-01148-w","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5043946650","display_name":"Zhaoping Wang","orcid":"https://orcid.org/0009-0007-6367-2161"},"institutions":[{"id":"https://openalex.org/I4210161528","display_name":"Qilu Hospital of Shandong University","ror":"https://ror.org/056ef9489","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210161528"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhaoping Wang","raw_affiliation_strings":["Department of Emergency, Qilu Hospital of Shandong University (Qingdao), Qingdao, China"],"raw_orcid":"https://orcid.org/0009-0007-6367-2161","affiliations":[{"raw_affiliation_string":"Department of Emergency, Qilu Hospital of Shandong University (Qingdao), Qingdao, China","institution_ids":["https://openalex.org/I4210161528"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102794584","display_name":"Zhiqiang Sun","orcid":"https://orcid.org/0000-0002-3815-8888"},"institutions":[{"id":"https://openalex.org/I108688024","display_name":"Qingdao University","ror":"https://ror.org/021cj6z65","country_code":"CN","type":"education","lineage":["https://openalex.org/I108688024"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiqiang Sun","raw_affiliation_strings":["School of Automation, Qingdao University, Qingdao, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Automation, Qingdao University, Qingdao, China","institution_ids":["https://openalex.org/I108688024"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5043946650"],"corresponding_institution_ids":["https://openalex.org/I4210161528"],"apc_list":{"value":1140,"currency":"GBP","value_usd":1398},"apc_paid":{"value":1140,"currency":"GBP","value_usd":1398},"fwci":6.1896,"has_fulltext":true,"cited_by_count":16,"citation_normalized_percentile":{"value":0.97002925,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":"2024","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12419","display_name":"Phonocardiography and Auscultation Techniques","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9908999800682068,"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/T12418","display_name":"Respiratory and Cough-Related Research","score":0.9839000105857849,"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/spectrogram","display_name":"Spectrogram","score":0.8112017512321472},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.7165817022323608},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6722352504730225},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.615827739238739},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5942190289497375},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.593653678894043},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.5927801132202148},{"id":"https://openalex.org/keywords/cepstrum","display_name":"Cepstrum","score":0.5876838564872742},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4685652554035187},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.46495044231414795},{"id":"https://openalex.org/keywords/mel-frequency-cepstrum","display_name":"Mel-frequency cepstrum","score":0.4276646673679352},{"id":"https://openalex.org/keywords/sensitivity","display_name":"Sensitivity (control systems)","score":0.4105467200279236},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.3682856559753418},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.10738179087638855}],"concepts":[{"id":"https://openalex.org/C45273575","wikidata":"https://www.wikidata.org/wiki/Q578970","display_name":"Spectrogram","level":2,"score":0.8112017512321472},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.7165817022323608},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6722352504730225},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.615827739238739},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5942190289497375},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.593653678894043},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.5927801132202148},{"id":"https://openalex.org/C88485024","wikidata":"https://www.wikidata.org/wiki/Q1054571","display_name":"Cepstrum","level":2,"score":0.5876838564872742},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4685652554035187},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.46495044231414795},{"id":"https://openalex.org/C151989614","wikidata":"https://www.wikidata.org/wiki/Q440370","display_name":"Mel-frequency cepstrum","level":3,"score":0.4276646673679352},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.4105467200279236},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3682856559753418},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.10738179087638855},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","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/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic 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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1186/s13634-024-01148-w","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s13634-024-01148-w","pdf_url":"https://asp-eurasipjournals.springeropen.com/counter/pdf/10.1186/s13634-024-01148-w","source":{"id":"https://openalex.org/S35920007","display_name":"EURASIP Journal on Advances in Signal Processing","issn_l":"1687-6172","issn":["1687-6172","1687-6180"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"EURASIP Journal on Advances in Signal Processing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:b17dda9006634f548ca59726c31af4ed","is_oa":true,"landing_page_url":"https://doaj.org/article/b17dda9006634f548ca59726c31af4ed","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":"EURASIP Journal on Advances in Signal Processing, Vol 2024, Iss 1, Pp 1-21 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1186/s13634-024-01148-w","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s13634-024-01148-w","pdf_url":"https://asp-eurasipjournals.springeropen.com/counter/pdf/10.1186/s13634-024-01148-w","source":{"id":"https://openalex.org/S35920007","display_name":"EURASIP Journal on Advances in Signal Processing","issn_l":"1687-6172","issn":["1687-6172","1687-6180"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"EURASIP Journal on Advances in Signal Processing","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4394808256.pdf"},"referenced_works_count":50,"referenced_works":["https://openalex.org/W2043392634","https://openalex.org/W2076866543","https://openalex.org/W2295562464","https://openalex.org/W2509065397","https://openalex.org/W2568314470","https://openalex.org/W2754136846","https://openalex.org/W2770588696","https://openalex.org/W2770896033","https://openalex.org/W2789847315","https://openalex.org/W2806006687","https://openalex.org/W2911746393","https://openalex.org/W2921177942","https://openalex.org/W2953914369","https://openalex.org/W2964770898","https://openalex.org/W3011086304","https://openalex.org/W3011926303","https://openalex.org/W3021778999","https://openalex.org/W3089867583","https://openalex.org/W3097359093","https://openalex.org/W3098357269","https://openalex.org/W3103935216","https://openalex.org/W3115941641","https://openalex.org/W3116715673","https://openalex.org/W3131682574","https://openalex.org/W3146081461","https://openalex.org/W3158732937","https://openalex.org/W3183475563","https://openalex.org/W3188992532","https://openalex.org/W3194600507","https://openalex.org/W3207400687","https://openalex.org/W3209660925","https://openalex.org/W3214429548","https://openalex.org/W4200341386","https://openalex.org/W4210329219","https://openalex.org/W4210428539","https://openalex.org/W4213086326","https://openalex.org/W4220965360","https://openalex.org/W4225669766","https://openalex.org/W4285503482","https://openalex.org/W4287372409","https://openalex.org/W4288437714","https://openalex.org/W4289711138","https://openalex.org/W4292429673","https://openalex.org/W4294891479","https://openalex.org/W4297229528","https://openalex.org/W4315487202","https://openalex.org/W4322763655","https://openalex.org/W4324116460","https://openalex.org/W4375947140","https://openalex.org/W6600847284"],"related_works":["https://openalex.org/W2018086531","https://openalex.org/W1980297060","https://openalex.org/W2387604097","https://openalex.org/W2373675101","https://openalex.org/W4385672897","https://openalex.org/W106160982","https://openalex.org/W2359140082","https://openalex.org/W2074132948","https://openalex.org/W2160511961","https://openalex.org/W4317383455"],"abstract_inverted_index":{"Abstract":[0],"It":[1,161],"is":[2,51,57,92,99,152,162,179,185],"desired":[3],"to":[4,10,127],"apply":[5],"deep":[6],"learning":[7],"models":[8],"(DLMs)":[9],"assist":[11],"physicians":[12],"in":[13],"distinguishing":[14],"abnormal/normal":[15],"lung":[16,61,80,121],"sounds":[17,62,81,122],"as":[18,20,59,125],"quickly":[19],"possible.":[21],"The":[22,64,113,131,149,155],"performance":[23,39,69,144,153,184],"of":[24,43,67,70,79,86,104,120,133],"DLMs":[25],"depends":[26],"on":[27,73,143],"feature-related":[28,41],"and":[29,40,89,96,110,115,135,176,188],"model-related":[30],"parameters":[31,42],"heavily.":[32],"In":[33],"this":[34],"paper,":[35],"the":[36,60,71,77,102,128,158,183],"relationship":[37],"between":[38],"a":[44],"DLM,":[45],"i.e.,":[46,76],"convolutional":[47],"neural":[48],"network":[49],"(CNN)":[50],"analyzed":[52],"through":[53],"experiments.":[54],"ICBHI":[55],"2017":[56],"selected":[58],"dataset.":[63],"sensitivity":[65],"analysis":[66],"classification":[68],"DLM":[72],"three":[74],"parameters,":[75],"length":[78],"frame,":[82],"overlap":[83],"percentage":[84],"(OP)":[85],"successive":[87],"frames":[88],"feature":[90,178],"type,":[91],"performed.":[93],"An":[94],"augmented":[95],"balanced":[97],"dataset":[98],"acquired":[100],"by":[101],"way":[103],"white":[105],"noise":[106],"addition,":[107],"time":[108],"stretching":[109],"pitch":[111],"shifting.":[112],"spectrogram":[114,177],"mel":[116],"frequency":[117,168],"cepstrum":[118],"coefficients":[119],"are":[123,195],"used":[124],"features":[126],"CNN,":[129],"respectively.":[130],"results":[132],"training":[134],"test":[136],"show":[137],"that":[138,164],"there":[139],"exists":[140],"significant":[141],"difference":[142],"among":[145],"various":[146],"parameter":[147,150],"combinations.":[148],"OP":[151,174],"sensitive.":[154],"higher":[156],"OP,":[157],"better":[159,187],"performance.":[160],"concluded":[163],"for":[165],"fixed":[166],"sampling":[167],"8":[169],"kHz,":[170],"frame":[171],"size":[172],"128,":[173],"75%":[175],"optimum":[180],"under":[181],"which":[182],"relatively":[186],"no":[189],"extra":[190],"computation":[191],"or":[192],"storage":[193],"resources":[194],"required.":[196]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":13},{"year":2024,"cited_by_count":1}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}