{"id":"https://openalex.org/W1544890851","doi":"https://doi.org/10.1109/hpcsim.2015.7237053","title":"Quartiles and Mel Frequency Cepstral Coefficients vectors in Hidden Markov-Gaussian Mixture Models classification of merged heart sounds and lung sounds signals","display_name":"Quartiles and Mel Frequency Cepstral Coefficients vectors in Hidden Markov-Gaussian Mixture Models classification of merged heart sounds and lung sounds signals","publication_year":2015,"publication_date":"2015-07-01","ids":{"openalex":"https://openalex.org/W1544890851","doi":"https://doi.org/10.1109/hpcsim.2015.7237053","mag":"1544890851"},"language":"en","primary_location":{"id":"doi:10.1109/hpcsim.2015.7237053","is_oa":false,"landing_page_url":"https://doi.org/10.1109/hpcsim.2015.7237053","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 International Conference on High Performance Computing &amp; Simulation (HPCS)","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/A5032492775","display_name":"Pedro Mayorga","orcid":"https://orcid.org/0000-0002-1889-6154"},"institutions":[{"id":"https://openalex.org/I3133460883","display_name":"Mexicali Institute of Technology","ror":"https://ror.org/03jfd4440","country_code":"MX","type":"education","lineage":["https://openalex.org/I3133460883"]}],"countries":["MX"],"is_corresponding":true,"raw_author_name":"Pedro Mayorga","raw_affiliation_strings":["Departamento de Posgrado, Instituto Tecnologico de Mexicali, Mexicali, Mexico","Departamento de Posgrado, Instituto Tecnol\u00f3gico de Mexicali, M\u00e9xico"],"affiliations":[{"raw_affiliation_string":"Departamento de Posgrado, Instituto Tecnologico de Mexicali, Mexicali, Mexico","institution_ids":["https://openalex.org/I3133460883"]},{"raw_affiliation_string":"Departamento de Posgrado, Instituto Tecnol\u00f3gico de Mexicali, M\u00e9xico","institution_ids":["https://openalex.org/I3133460883"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006684507","display_name":"Daniela Ibarra","orcid":"https://orcid.org/0009-0003-3769-355X"},"institutions":[{"id":"https://openalex.org/I3133460883","display_name":"Mexicali Institute of Technology","ror":"https://ror.org/03jfd4440","country_code":"MX","type":"education","lineage":["https://openalex.org/I3133460883"]}],"countries":["MX"],"is_corresponding":false,"raw_author_name":"Daniela Ibarra","raw_affiliation_strings":["Departamento de Posgrado, Instituto Tecnologico de Mexicali, Mexicali, Mexico","Departamento de Posgrado, Instituto Tecnol\u00f3gico de Mexicali, M\u00e9xico"],"affiliations":[{"raw_affiliation_string":"Departamento de Posgrado, Instituto Tecnologico de Mexicali, Mexicali, Mexico","institution_ids":["https://openalex.org/I3133460883"]},{"raw_affiliation_string":"Departamento de Posgrado, Instituto Tecnol\u00f3gico de Mexicali, M\u00e9xico","institution_ids":["https://openalex.org/I3133460883"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111764772","display_name":"Vesna Zeljkovi\u0107","orcid":null},"institutions":[{"id":"https://openalex.org/I33821262","display_name":"Lincoln University - Pennsylvania","ror":"https://ror.org/0521rfb23","country_code":"US","type":"education","lineage":["https://openalex.org/I33821262"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Vesna Zeljkovic","raw_affiliation_strings":["College of Science and Technology, The Lincoln University, Pennsylvania, USA","[College of Science and Technology, The Lincoln University, Pennsylvania, USA]"],"affiliations":[{"raw_affiliation_string":"College of Science and Technology, The Lincoln University, Pennsylvania, USA","institution_ids":["https://openalex.org/I33821262"]},{"raw_affiliation_string":"[College of Science and Technology, The Lincoln University, Pennsylvania, USA]","institution_ids":["https://openalex.org/I33821262"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086674022","display_name":"C. Druzgalski","orcid":null},"institutions":[{"id":"https://openalex.org/I59897056","display_name":"California State University, Long Beach","ror":"https://ror.org/0080fxk18","country_code":"US","type":"education","lineage":["https://openalex.org/I59897056"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Christopher Druzgalski","raw_affiliation_strings":["Electrical Engineering Department, California State University, Long Beach, CA, USA","Electrical Engineering Department, California State University, Long Beach, USA"],"affiliations":[{"raw_affiliation_string":"Electrical Engineering Department, California State University, Long Beach, CA, USA","institution_ids":["https://openalex.org/I59897056"]},{"raw_affiliation_string":"Electrical Engineering Department, California State University, Long Beach, USA","institution_ids":["https://openalex.org/I59897056"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5032492775"],"corresponding_institution_ids":["https://openalex.org/I3133460883"],"apc_list":null,"apc_paid":null,"fwci":0.467,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.6770683,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"183","issue":null,"first_page":"298","last_page":"304"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12419","display_name":"Phonocardiography and Auscultation Techniques","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9889000058174133,"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9682000279426575,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.8539149165153503},{"id":"https://openalex.org/keywords/mel-frequency-cepstrum","display_name":"Mel-frequency cepstrum","score":0.7834662199020386},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7484506964683533},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.7431311011314392},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.589774489402771},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.56820148229599},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5586298704147339},{"id":"https://openalex.org/keywords/akaike-information-criterion","display_name":"Akaike information criterion","score":0.45016464591026306},{"id":"https://openalex.org/keywords/bayesian-information-criterion","display_name":"Bayesian information criterion","score":0.4425249695777893},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.43536484241485596},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.42668431997299194},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.16622525453567505}],"concepts":[{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.8539149165153503},{"id":"https://openalex.org/C151989614","wikidata":"https://www.wikidata.org/wiki/Q440370","display_name":"Mel-frequency cepstrum","level":3,"score":0.7834662199020386},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7484506964683533},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.7431311011314392},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.589774489402771},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.56820148229599},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5586298704147339},{"id":"https://openalex.org/C126674687","wikidata":"https://www.wikidata.org/wiki/Q1662573","display_name":"Akaike information criterion","level":2,"score":0.45016464591026306},{"id":"https://openalex.org/C168136583","wikidata":"https://www.wikidata.org/wiki/Q1988242","display_name":"Bayesian information criterion","level":2,"score":0.4425249695777893},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.43536484241485596},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.42668431997299194},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.16622525453567505}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/hpcsim.2015.7237053","is_oa":false,"landing_page_url":"https://doi.org/10.1109/hpcsim.2015.7237053","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 International Conference on High Performance Computing &amp; Simulation (HPCS)","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/W174669002","https://openalex.org/W1560013842","https://openalex.org/W1588351438","https://openalex.org/W1622964048","https://openalex.org/W1802587561","https://openalex.org/W1882502389","https://openalex.org/W1940288739","https://openalex.org/W1982322018","https://openalex.org/W2014700686","https://openalex.org/W2046614261","https://openalex.org/W2054475529","https://openalex.org/W2059187471","https://openalex.org/W2071797156","https://openalex.org/W2080800567","https://openalex.org/W2086036685","https://openalex.org/W2113626315","https://openalex.org/W2118124255","https://openalex.org/W2127838976","https://openalex.org/W2128559410","https://openalex.org/W2133069808","https://openalex.org/W2134701171","https://openalex.org/W2134852568","https://openalex.org/W2150953991","https://openalex.org/W2168517158","https://openalex.org/W2172401215","https://openalex.org/W2476301878","https://openalex.org/W2489784804","https://openalex.org/W2996883695","https://openalex.org/W3146003712","https://openalex.org/W4206979872","https://openalex.org/W4246639841","https://openalex.org/W6607140380","https://openalex.org/W6679756777"],"related_works":["https://openalex.org/W1566728629","https://openalex.org/W2617987378","https://openalex.org/W2509820422","https://openalex.org/W1502087262","https://openalex.org/W3187368641","https://openalex.org/W2626271282","https://openalex.org/W3122388856","https://openalex.org/W1946191587","https://openalex.org/W2088726129","https://openalex.org/W1998988312"],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"integrated":[3],"Hidden":[4],"Markov":[5],"and":[6,16,33,51,59,119],"Gaussian":[7],"Mixture":[8],"Models":[9],"(HMM-GMM)":[10],"to":[11,23,93,129],"classify":[12],"lung":[13],"sounds":[14,18],"(LS)":[15],"heart":[17],"(HS)":[19],"characteristics.":[20],"In":[21,78],"order":[22],"optimize":[24],"the":[25,34,49,69,96,103,112,123],"models'":[26,124],"size,":[27],"several":[28],"methodologies":[29],"encompassing":[30],"dendrograms,":[31,117],"silhouettes":[32],"Bayesian":[35],"Information":[36],"Criterion":[37],"(BIC)":[38],"were":[39,43,106],"applied.":[40],"The":[41,64],"experiments":[42],"carried":[44],"out":[45],"extracting":[46],"features":[47],"from":[48],"LS":[50],"HS":[52],"with":[53,122],"MFCC":[54,102],"(Mel-Frequency":[55],"Cepstral":[56],"Coefficients)":[57],"vectors":[58],"Quantile":[60],"vectors,":[61,82],"specifically":[62],"Quartiles.":[63],"merged":[65,133],"HMM-GMM":[66,134],"architecture":[67],"for":[68,95],"signals":[70],"using":[71,116],"Quartiles,":[72],"overall":[73],"offered":[74],"consistent":[75],"classification":[76,87,104,138],"results.":[77],"both":[79],"types":[80],"of":[81,86,99,111,114,132,139],"a":[83],"high":[84],"degree":[85],"efficiency":[88,131],"was":[89],"obtained":[90],"reaching":[91],"up":[92],"96%":[94],"studied":[97],"sets":[98],"signals.":[100,142],"For":[101],"results":[105],"not":[107],"conclusive.":[108],"An":[109],"assessment":[110],"number":[113],"clusters":[115],"silhouettes,":[118],"BIC":[120],"linked":[121],"size.":[125],"Consequently":[126],"this":[127],"allows":[128],"enhance":[130],"models":[135],"in":[136],"diagnostic":[137],"cardiopulmonary":[140],"acoustic":[141]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1},{"year":2016,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
