{"id":"https://openalex.org/W3135849576","doi":"https://doi.org/10.1109/access.2021.3063129","title":"CardioXNet: A Novel Lightweight Deep Learning Framework for Cardiovascular Disease Classification Using Heart Sound Recordings","display_name":"CardioXNet: A Novel Lightweight Deep Learning Framework for Cardiovascular Disease Classification Using Heart Sound Recordings","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3135849576","doi":"https://doi.org/10.1109/access.2021.3063129","mag":"3135849576"},"language":"en","primary_location":{"id":"doi:10.1109/access.2021.3063129","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3063129","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09366875.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09366875.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5079610013","display_name":"Samiul Based Shuvo","orcid":"https://orcid.org/0000-0002-5035-2114"},"institutions":[{"id":"https://openalex.org/I183697816","display_name":"Bangladesh University of Engineering and Technology","ror":"https://ror.org/05a1qpv97","country_code":"BD","type":"education","lineage":["https://openalex.org/I183697816"]}],"countries":["BD"],"is_corresponding":true,"raw_author_name":"Samiul Based Shuvo","raw_affiliation_strings":["Bangladesh University of Engineering and Technology, Dhaka, Bangladesh"],"raw_orcid":"https://orcid.org/0000-0002-5035-2114","affiliations":[{"raw_affiliation_string":"Bangladesh University of Engineering and Technology, Dhaka, Bangladesh","institution_ids":["https://openalex.org/I183697816"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011561014","display_name":"Shams Nafisa Ali","orcid":"https://orcid.org/0000-0002-5213-5463"},"institutions":[{"id":"https://openalex.org/I183697816","display_name":"Bangladesh University of Engineering and Technology","ror":"https://ror.org/05a1qpv97","country_code":"BD","type":"education","lineage":["https://openalex.org/I183697816"]}],"countries":["BD"],"is_corresponding":false,"raw_author_name":"Shams Nafisa Ali","raw_affiliation_strings":["Bangladesh University of Engineering and Technology, Dhaka, Bangladesh"],"raw_orcid":"https://orcid.org/0000-0002-5213-5463","affiliations":[{"raw_affiliation_string":"Bangladesh University of Engineering and Technology, Dhaka, Bangladesh","institution_ids":["https://openalex.org/I183697816"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000612202","display_name":"Soham Irtiza Swapnil","orcid":"https://orcid.org/0000-0002-2379-4646"},"institutions":[{"id":"https://openalex.org/I183697816","display_name":"Bangladesh University of Engineering and Technology","ror":"https://ror.org/05a1qpv97","country_code":"BD","type":"education","lineage":["https://openalex.org/I183697816"]}],"countries":["BD"],"is_corresponding":false,"raw_author_name":"Soham Irtiza Swapnil","raw_affiliation_strings":["Bangladesh University of Engineering and Technology, Dhaka, Bangladesh"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Bangladesh University of Engineering and Technology, Dhaka, Bangladesh","institution_ids":["https://openalex.org/I183697816"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017023860","display_name":"Mabrook Al\u2010Rakhami","orcid":"https://orcid.org/0000-0001-5343-8370"},"institutions":[{"id":"https://openalex.org/I28022161","display_name":"King Saud University","ror":"https://ror.org/02f81g417","country_code":"SA","type":"education","lineage":["https://openalex.org/I28022161"]}],"countries":["SA"],"is_corresponding":false,"raw_author_name":"Mabrook S. Al-Rakhami","raw_affiliation_strings":["Research Chair of Pervasive and Mobile Computing, King Saud University, Riyadh, Saudi Arabia"],"raw_orcid":"https://orcid.org/0000-0001-5343-8370","affiliations":[{"raw_affiliation_string":"Research Chair of Pervasive and Mobile Computing, King Saud University, Riyadh, Saudi Arabia","institution_ids":["https://openalex.org/I28022161"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5043393776","display_name":"Abdu Gumaei","orcid":"https://orcid.org/0000-0001-8512-9687"},"institutions":[{"id":"https://openalex.org/I28022161","display_name":"King Saud University","ror":"https://ror.org/02f81g417","country_code":"SA","type":"education","lineage":["https://openalex.org/I28022161"]},{"id":"https://openalex.org/I36197038","display_name":"Taiz University","ror":"https://ror.org/03jwcxq96","country_code":"YE","type":"education","lineage":["https://openalex.org/I36197038"]}],"countries":["SA","YE"],"is_corresponding":false,"raw_author_name":"Abdu Gumaei","raw_affiliation_strings":["Faculty of Applied Science, Taiz University, Taiz, Yemen","Research Chair of Pervasive and Mobile Computing, King Saud University, Riyadh, Saudi Arabia"],"raw_orcid":"https://orcid.org/0000-0001-8512-9687","affiliations":[{"raw_affiliation_string":"Faculty of Applied Science, Taiz University, Taiz, Yemen","institution_ids":["https://openalex.org/I36197038"]},{"raw_affiliation_string":"Research Chair of Pervasive and Mobile Computing, King Saud University, Riyadh, Saudi Arabia","institution_ids":["https://openalex.org/I28022161"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5079610013"],"corresponding_institution_ids":["https://openalex.org/I183697816"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":19.5359,"has_fulltext":true,"cited_by_count":195,"citation_normalized_percentile":{"value":0.99603395,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"9","issue":null,"first_page":"36955","last_page":"36967"},"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7794827222824097},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5642305016517639},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5485866069793701},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.5331736207008362},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5302091836929321},{"id":"https://openalex.org/keywords/phonocardiogram","display_name":"Phonocardiogram","score":0.5209097266197205},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.489847868680954},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.44250404834747314},{"id":"https://openalex.org/keywords/auscultation","display_name":"Auscultation","score":0.4415857791900635},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.41310715675354004},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.35928577184677124},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.29336220026016235},{"id":"https://openalex.org/keywords/cardiology","display_name":"Cardiology","score":0.13889646530151367},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.09215313196182251}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7794827222824097},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5642305016517639},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5485866069793701},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.5331736207008362},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5302091836929321},{"id":"https://openalex.org/C159693508","wikidata":"https://www.wikidata.org/wiki/Q3301075","display_name":"Phonocardiogram","level":2,"score":0.5209097266197205},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.489847868680954},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.44250404834747314},{"id":"https://openalex.org/C2777324038","wikidata":"https://www.wikidata.org/wiki/Q779054","display_name":"Auscultation","level":2,"score":0.4415857791900635},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.41310715675354004},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.35928577184677124},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.29336220026016235},{"id":"https://openalex.org/C164705383","wikidata":"https://www.wikidata.org/wiki/Q10379","display_name":"Cardiology","level":1,"score":0.13889646530151367},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.09215313196182251}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/access.2021.3063129","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3063129","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09366875.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:2010.01392","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2010.01392","pdf_url":"https://arxiv.org/pdf/2010.01392","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:doaj.org/article:223c3a9ed8db41068527874be136d00d","is_oa":true,"landing_page_url":"https://doaj.org/article/223c3a9ed8db41068527874be136d00d","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":"IEEE Access, Vol 9, Pp 36955-36967 (2021)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2021.3063129","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3063129","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09366875.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","score":0.8399999737739563,"display_name":"Good health and well-being"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320325821","display_name":"Bangladesh University of Engineering and Technology","ror":"https://ror.org/05a1qpv97"},{"id":"https://openalex.org/F4320335726","display_name":"Deanship of Scientific Research, King Saud University","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3135849576.pdf","grobid_xml":"https://content.openalex.org/works/W3135849576.grobid-xml"},"referenced_works_count":61,"referenced_works":["https://openalex.org/W1538131130","https://openalex.org/W1753482797","https://openalex.org/W1836465849","https://openalex.org/W1921523184","https://openalex.org/W1944597394","https://openalex.org/W1987001343","https://openalex.org/W2010662891","https://openalex.org/W2022139970","https://openalex.org/W2024511442","https://openalex.org/W2039031643","https://openalex.org/W2048310347","https://openalex.org/W2064675550","https://openalex.org/W2078978121","https://openalex.org/W2082567546","https://openalex.org/W2131774270","https://openalex.org/W2263065115","https://openalex.org/W2279098554","https://openalex.org/W2516413581","https://openalex.org/W2524428287","https://openalex.org/W2540718148","https://openalex.org/W2557139718","https://openalex.org/W2604096629","https://openalex.org/W2607377556","https://openalex.org/W2789524143","https://openalex.org/W2792997810","https://openalex.org/W2799187413","https://openalex.org/W2804483946","https://openalex.org/W2805571860","https://openalex.org/W2899929647","https://openalex.org/W2901976219","https://openalex.org/W2902922150","https://openalex.org/W2906028509","https://openalex.org/W2944090083","https://openalex.org/W2949117887","https://openalex.org/W2953974830","https://openalex.org/W2958640613","https://openalex.org/W2963163009","https://openalex.org/W2963705844","https://openalex.org/W2963838685","https://openalex.org/W2966145183","https://openalex.org/W2979158918","https://openalex.org/W2981505513","https://openalex.org/W3003982335","https://openalex.org/W3004025693","https://openalex.org/W3014556950","https://openalex.org/W3023757337","https://openalex.org/W3035159033","https://openalex.org/W3039983345","https://openalex.org/W3055801575","https://openalex.org/W3116715673","https://openalex.org/W3122189984","https://openalex.org/W4285719527","https://openalex.org/W4289232656","https://openalex.org/W6632100814","https://openalex.org/W6637698695","https://openalex.org/W6638667902","https://openalex.org/W6640185926","https://openalex.org/W6726047775","https://openalex.org/W6727208969","https://openalex.org/W6756345725","https://openalex.org/W6782790884"],"related_works":["https://openalex.org/W2066804906","https://openalex.org/W2400642710","https://openalex.org/W1978202793","https://openalex.org/W22400918","https://openalex.org/W1922887185","https://openalex.org/W3191193899","https://openalex.org/W2898768363","https://openalex.org/W4323341785","https://openalex.org/W3152780454","https://openalex.org/W2917737190"],"abstract_inverted_index":{"The":[0,74,175,269],"alarmingly":[1],"high":[2,270],"mortality":[3],"rate":[4],"and":[5,35,66,89,109,115,141,156,207,261,276,287],"increasing":[6],"global":[7],"prevalence":[8],"of":[9,52,55,82,153,246,257,267,285,299],"cardiovascular":[10],"diseases":[11],"(CVDs)":[12],"signify":[13],"the":[14,80,101,107,113,118,131,135,147,154,157,160,173,180,189,194,225,233,249,292],"crucial":[15],"need":[16],"for":[17,49,297],"early":[18],"detection":[19,51],"schemes.":[20],"Phonocardiogram":[21],"(PCG)":[22],"signals":[23],"have":[24,97],"been":[25,77,98],"historically":[26],"applied":[27],"in":[28,100,130,187,303],"this":[29,38],"domain":[30],"owing":[31],"to":[32,105,116,193,200],"its":[33],"simplicity":[34],"cost-effectiveness.":[36],"In":[37,146],"article,":[39],"we":[40],"propose":[41],"CardioXNet,":[42],"a":[43,229,254,281],"novel":[44],"lightweight":[45],"end-to-end":[46,182,288],"CRNN":[47],"architecture":[48,183],"automatic":[50],"five":[53],"classes":[54],"cardiac":[56],"auscultation":[57],"namely":[58],"normal,":[59],"aortic":[60],"stenosis,":[61,63],"mitral":[62,64,67],"regurgitation":[65],"valve":[68],"prolapse":[69],"using":[70,224,307],"raw":[71],"PCG":[72,114,259],"signal.":[73,174],"process":[75],"has":[76],"automated":[78],"by":[79,228],"involvement":[81],"two":[83],"learning":[84,88,103,133,150],"phases":[85],"namely,":[86],"representation":[87,102,132],"sequence":[90],"residual":[91,149],"learning.":[92],"Three":[93],"parallel":[94],"CNN":[95],"pathways":[96],"implemented":[99],"phase":[104],"learn":[106],"coarse":[108],"fine-grained":[110],"features":[111,120,140,166],"from":[112,121],"explore":[117],"salient":[119],"variable":[122],"receptive":[123],"fields":[124],"involving":[125],"2D-CNN":[126],"based":[127],"squeeze-expansion.":[128],"Thus,":[129],"phase,":[134,151],"network":[136,161,294],"extracts":[137],"efficient":[138],"time-invariant":[139],"converges":[142],"with":[143,198,280],"great":[144],"rapidity.":[145],"sequential":[148],"because":[152],"bidirectional-LSTMs":[155],"skip":[158],"connection,":[159],"can":[162],"proficiently":[163],"extract":[164],"temporal":[165],"without":[167],"performing":[168],"any":[169,221],"feature":[170],"extraction":[171],"on":[172,211,238,253,273],"obtained":[176],"results":[177],"demonstrate":[178],"that":[179],"proposed":[181,234,293],"yields":[184],"outstanding":[185],"performance":[186],"all":[188],"evaluation":[190],"metrics":[191,272],"compared":[192],"previous":[195,222],"state-of-the-art":[196],"methods":[197],"up":[199],"99.60%":[201],"accuracy,":[202],"99.56%":[203],"precision,":[204],"99.52%":[205],"recall":[206],"99.68%":[208],"F1-":[209],"score":[210],"an":[212,244],"average":[213],"while":[214],"being":[215],"computationally":[216],"comparable.":[217],"This":[218],"model":[219,235,250],"outperforms":[220],"works":[223],"same":[226],"database":[227],"considerable":[230],"margin.":[231],"Moreover,":[232],"was":[236,251],"tested":[237],"PhysioNet/CinC":[239],"2016":[240],"challenge":[241],"dataset":[242,256,260,263,278],"achieving":[243,264],"accuracy":[245,266,271],"86.57%.":[247],"Finally":[248],"evaluated":[252],"merged":[255],"Github":[258],"PhysioNet":[262],"excellent":[265],"88.09%.":[268],"both":[274],"primary":[275],"secondary":[277],"combined":[279],"significantly":[282],"low":[283,304],"number":[284],"parameters":[286],"prediction":[289],"approach":[290],"makes":[291],"especially":[295],"suitable":[296],"point":[298],"care":[300],"CVD":[301],"screening":[302],"resource":[305],"setups":[306],"memory":[308],"constraint":[309],"mobile":[310],"devices.":[311]},"counts_by_year":[{"year":2026,"cited_by_count":12},{"year":2025,"cited_by_count":49},{"year":2024,"cited_by_count":43},{"year":2023,"cited_by_count":42},{"year":2022,"cited_by_count":36},{"year":2021,"cited_by_count":13}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}