{"id":"https://openalex.org/W3014826487","doi":"https://doi.org/10.3906/elk-1905-165","title":"Short unsegmented PCG classification based on ensemble classifier","display_name":"Short unsegmented PCG classification based on ensemble classifier","publication_year":2020,"publication_date":"2020-01-14","ids":{"openalex":"https://openalex.org/W3014826487","doi":"https://doi.org/10.3906/elk-1905-165","mag":"3014826487"},"language":"en","primary_location":{"id":"doi:10.3906/elk-1905-165","is_oa":false,"landing_page_url":"https://doi.org/10.3906/elk-1905-165","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 & 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/A5010510906","display_name":"Sinam Ajitkumar Singh","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Sinam Ajitkumar SINGH","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5064024856","display_name":"Swanirbhar Majumder","orcid":"https://orcid.org/0000-0002-1046-1682"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Swanirbhar MAJUMDER","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5010510906"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.7723,"has_fulltext":false,"cited_by_count":28,"citation_normalized_percentile":{"value":0.87493054,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"28","issue":"2","first_page":"875","last_page":"889"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12419","display_name":"Phonocardiography and Auscultation Techniques","score":0.9993000030517578,"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":0.9993000030517578,"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":0.9624999761581421,"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/T10863","display_name":"Voice and Speech Disorders","score":0.9409000277519226,"subfield":{"id":"https://openalex.org/subfields/2737","display_name":"Physiology"},"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/computer-science","display_name":"Computer science","score":0.742622971534729},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7342194318771362},{"id":"https://openalex.org/keywords/mel-frequency-cepstrum","display_name":"Mel-frequency cepstrum","score":0.721244752407074},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6616733074188232},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.6534563302993774},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6207662224769592},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5937698483467102},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5259243249893188},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5027413368225098},{"id":"https://openalex.org/keywords/sensitivity","display_name":"Sensitivity (control systems)","score":0.42056357860565186},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.32006537914276123}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.742622971534729},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7342194318771362},{"id":"https://openalex.org/C151989614","wikidata":"https://www.wikidata.org/wiki/Q440370","display_name":"Mel-frequency cepstrum","level":3,"score":0.721244752407074},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6616733074188232},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.6534563302993774},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6207662224769592},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5937698483467102},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5259243249893188},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5027413368225098},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.42056357860565186},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.32006537914276123},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3906/elk-1905-165","is_oa":false,"landing_page_url":"https://doi.org/10.3906/elk-1905-165","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 & COMPUTER SCIENCES","raw_type":"journal-article"},{"id":"pmh:oai:dergipark.org.tr:article/725071","is_oa":false,"landing_page_url":"https://dergipark.org.tr/tr/pub/tbtkelektrik/issue/53851/725071","pdf_url":null,"source":{"id":"https://openalex.org/S4306401840","display_name":"DergiPark (Istanbul University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I67581229","host_organization_name":"Istanbul University","host_organization_lineage":["https://openalex.org/I67581229"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"\\n                                                                    Volume: 28, Issue: 2\\n                                                                                                    875-889\\n                                                                \\n                            ","raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3","score":0.6100000143051147}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2125652721","https://openalex.org/W1540371141","https://openalex.org/W1549363203","https://openalex.org/W4317383455","https://openalex.org/W2548511587","https://openalex.org/W4293232884","https://openalex.org/W2422472940","https://openalex.org/W2019475500","https://openalex.org/W2548162870","https://openalex.org/W2138847091"],"abstract_inverted_index":{"Diseases":[0],"associated":[1],"with":[2,182],"the":[3,8,18,64,77,83,86,99,113,150],"heart":[4,19,30,42,54,108],"are":[5,147],"one":[6],"of":[7,11,17,24,29,41,53,58,76,85,180,185,189],"main":[9],"reasons":[10],"death":[12],"worldwide.":[13],"Hence,":[14],"early":[15],"examination":[16],"is":[20,32,44,126,167,191],"important.":[21],"For":[22],"analysis":[23],"cardiac":[25],"disorders,":[26],"a":[27,33,45],"study":[28,125],"sounds":[31,43,55],"crucial":[34],"and":[35,56,94,140,187],"beneficial":[36],"approach.":[37],"Still,":[38],"automated":[39,107],"classification":[40,69,87,110],"challenging":[46],"task":[47],"that":[48,200],"mainly":[49],"depends":[50],"on":[51,81,90,128,155],"segmentation":[52,92,114],"derivation":[57],"features":[59,146],"using":[60,116,149,169,193],"segmented":[61],"samples.":[62],"In":[63,101],"literature":[65],"available":[66],"for":[67],"PCG":[68],"provided":[70],"by":[71,111],"PhysioNet/CinC":[72],"Challenge":[73,177],"2016,":[74],"most":[75],"research":[78],"has":[79,95],"focused":[80],"enhancing":[82],"accuracy":[84],"model":[88,203],"based":[89,127,154],"complicated":[91],"processes":[93],"failed":[96],"to":[97],"improve":[98],"sensitivity.":[100,123],"this":[102],"paper,":[103],"we":[104],"present":[105],"an":[106,156],"sound":[109],"eliminating":[112],"steps":[115],"multidomain":[117,141],"features,":[118],"which":[119],"results":[120],"in":[121,173,175],"enhanced":[122],"The":[124,144,196],"homomorphic":[129],"envelogram,":[130],"mel":[131],"frequency":[132],"cepstral":[133],"coefficient":[134],"(MFCC),":[135],"power":[136],"spectral":[137],"density":[138],"(PSD),":[139],"feature":[142],"extraction.":[143],"extracted":[145],"trained":[148],"5-fold":[151],"cross-validation":[152],"method":[153],"ensemble":[157],"boosting":[158],"algorithm":[159],"over":[160],"100":[161],"independent":[162],"iterations.":[163],"Our":[164],"proposed":[165,202],"design":[166],"evaluated":[168],"public":[170],"datasets":[171],"published":[172],"PhysioNet/Computers":[174],"Cardiology":[176],"2016.":[178],"Accuracy":[179],"92.47%":[181],"improved":[183],"sensitivity":[184],"94.08%":[186],"specificity":[188],"91.95%":[190],"achieved":[192],"our":[194,201],"model.":[195],"output":[197],"performance":[198,206],"proves":[199],"offers":[204],"superior":[205],"results.":[207]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":1}],"updated_date":"2026-03-22T08:09:32.410652","created_date":"2025-10-10T00:00:00"}