{"id":"https://openalex.org/W4385624274","doi":"https://doi.org/10.1109/eurocon56442.2023.10199054","title":"Variational Mode Decomposition and a Light CNN-LSTM Model for Classification of Heart Sound Signals","display_name":"Variational Mode Decomposition and a Light CNN-LSTM Model for Classification of Heart Sound Signals","publication_year":2023,"publication_date":"2023-07-06","ids":{"openalex":"https://openalex.org/W4385624274","doi":"https://doi.org/10.1109/eurocon56442.2023.10199054"},"language":"en","primary_location":{"id":"doi:10.1109/eurocon56442.2023.10199054","is_oa":false,"landing_page_url":"https://doi.org/10.1109/eurocon56442.2023.10199054","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE EUROCON 2023 - 20th International Conference on Smart Technologies","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/A5007282822","display_name":"Mahmoud Fakhry","orcid":"https://orcid.org/0000-0002-2170-2998"},"institutions":[{"id":"https://openalex.org/I50357001","display_name":"Universidad Carlos III de Madrid","ror":"https://ror.org/03ths8210","country_code":"ES","type":"education","lineage":["https://openalex.org/I50357001"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Mahmoud Fakhry","raw_affiliation_strings":["Carlos III University of Madrid,Department of Signal Theory and Communications,Legan&#x00E9;s (Madrid),Spain,28911"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Carlos III University of Madrid,Department of Signal Theory and Communications,Legan&#x00E9;s (Madrid),Spain,28911","institution_ids":["https://openalex.org/I50357001"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5065719579","display_name":"Ascensi\u00f3n Gallardo-Antol\u00edn","orcid":"https://orcid.org/0000-0002-9322-3128"},"institutions":[{"id":"https://openalex.org/I50357001","display_name":"Universidad Carlos III de Madrid","ror":"https://ror.org/03ths8210","country_code":"ES","type":"education","lineage":["https://openalex.org/I50357001"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Ascensi\u00f3n Gallardo-Antol\u00edn","raw_affiliation_strings":["Carlos III University of Madrid,Department of Signal Theory and Communications,Legan&#x00E9;s (Madrid),Spain,28911"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Carlos III University of Madrid,Department of Signal Theory and Communications,Legan&#x00E9;s (Madrid),Spain,28911","institution_ids":["https://openalex.org/I50357001"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.9002,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.86741146,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"295","last_page":"300"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12419","display_name":"Phonocardiography and Auscultation Techniques","score":1.0,"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":1.0,"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/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9394999742507935,"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"}},{"id":"https://openalex.org/T11021","display_name":"ECG Monitoring and Analysis","score":0.9296000003814697,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7464426755905151},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7217577695846558},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6686394214630127},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5758610367774963},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5536271333694458},{"id":"https://openalex.org/keywords/mode","display_name":"Mode (computer interface)","score":0.4531049132347107},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4278932213783264},{"id":"https://openalex.org/keywords/audio-signal","display_name":"Audio signal","score":0.4212228059768677},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.41606682538986206}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7464426755905151},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7217577695846558},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6686394214630127},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5758610367774963},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5536271333694458},{"id":"https://openalex.org/C48677424","wikidata":"https://www.wikidata.org/wiki/Q6888088","display_name":"Mode (computer interface)","level":2,"score":0.4531049132347107},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4278932213783264},{"id":"https://openalex.org/C64922751","wikidata":"https://www.wikidata.org/wiki/Q4650799","display_name":"Audio signal","level":3,"score":0.4212228059768677},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.41606682538986206},{"id":"https://openalex.org/C13895895","wikidata":"https://www.wikidata.org/wiki/Q3270773","display_name":"Speech coding","level":2,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/eurocon56442.2023.10199054","is_oa":false,"landing_page_url":"https://doi.org/10.1109/eurocon56442.2023.10199054","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE EUROCON 2023 - 20th International Conference on Smart Technologies","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3","score":0.5799999833106995}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W22400918","https://openalex.org/W1513801117","https://openalex.org/W1576563120","https://openalex.org/W2000982976","https://openalex.org/W2149168670","https://openalex.org/W2184139745","https://openalex.org/W2622526092","https://openalex.org/W2770877241","https://openalex.org/W2779637118","https://openalex.org/W2801060964","https://openalex.org/W2804483946","https://openalex.org/W2901976219","https://openalex.org/W2919115771","https://openalex.org/W2962948887","https://openalex.org/W3005771459","https://openalex.org/W3042078158","https://openalex.org/W3122189984","https://openalex.org/W3165504692","https://openalex.org/W4220822600","https://openalex.org/W4220897926","https://openalex.org/W4221136339","https://openalex.org/W4243513962","https://openalex.org/W4292608019","https://openalex.org/W4296713817","https://openalex.org/W6600942589","https://openalex.org/W6634677128"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W4321487865","https://openalex.org/W4313906399","https://openalex.org/W4391266461","https://openalex.org/W2590798552","https://openalex.org/W2811106690","https://openalex.org/W4239306820","https://openalex.org/W2947043951","https://openalex.org/W4399188509","https://openalex.org/W4312417841"],"abstract_inverted_index":{"Several":[0],"cardiovascular":[1],"diseases":[2],"(CVDs)":[3],"produce":[4],"changes":[5],"in":[6,94],"heart":[7,26,139],"sounds":[8],"and":[9,60,69,87,120],"murmurs.":[10],"This":[11],"paper":[12],"presents":[13],"the":[14,19,40,57,79,82,88,108,129,146,154],"VMD-CNN-LSTM":[15],"system":[16,114,130,147],"developed":[17,113],"for":[18,37,67],"diagnosis":[20],"of":[21,25,39,49,128,136,161],"CVDs":[22],"by":[23,42,101],"classification":[24,123,126],"sound":[27],"signals.":[28],"We":[29,53],"use":[30,61],"variational":[31],"mode":[32,51,58,110],"decomposition":[33],"(VMD)":[34],"to":[35,55,63,107],"compensate":[36],"nonstationarity":[38],"signal":[41],"decomposing":[43],"acquired":[44],"signals":[45],"into":[46],"a":[47,71,103,116,134,149],"set":[48],"intrinsic":[50],"functions.":[52,111],"propose":[54],"scale":[56],"functions":[59],"them":[62],"calculate":[64],"input":[65],"features":[66,98],"training":[68],"evaluating":[70],"classifier.":[72],"A":[73],"light":[74],"CNN-LSTM":[75],"model":[76],"used":[77,153],"as":[78],"classifier":[80],"integrates":[81],"convolutional":[83],"neural":[84],"network":[85,92,118],"(CNN)":[86],"long":[89],"short-term":[90],"memory":[91],"(LSTM)":[93],"one":[95],"architecture.":[96],"The":[97,112,125,141],"are":[99],"extracted":[100],"applying":[102],"weighted":[104],"logarithmic":[105],"operation":[106],"scaled":[109],"uses":[115],"few":[117],"layers":[119],"achieves":[121],"efficient":[122],"tasks.":[124],"performance":[127],"is":[131],"evaluated":[132],"using":[133],"database":[135],"five":[137],"valvular":[138],"conditions.":[140],"experimental":[142],"results":[143],"show":[144],"that":[145,152],"outperforms":[148],"recent":[150],"method":[151],"same":[155],"database,":[156],"with":[157],"an":[158],"average":[159],"accuracy":[160],"98.65%.":[162]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
