{"id":"https://openalex.org/W4392445481","doi":"https://doi.org/10.1145/3616901.3616907","title":"Cardiovascular Disease Detection Based on Multi-Modal Data Fusion and Multi-Branch Residual Network","display_name":"Cardiovascular Disease Detection Based on Multi-Modal Data Fusion and Multi-Branch Residual Network","publication_year":2023,"publication_date":"2023-04-14","ids":{"openalex":"https://openalex.org/W4392445481","doi":"https://doi.org/10.1145/3616901.3616907"},"language":"en","primary_location":{"id":"doi:10.1145/3616901.3616907","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3616901.3616907","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 International Conference on Frontiers of Artificial Intelligence and Machine Learning","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/A5064873899","display_name":"Jia Yuan Zhu","orcid":null},"institutions":[{"id":"https://openalex.org/I152269853","display_name":"Qilu University of Technology","ror":"https://ror.org/04hyzq608","country_code":"CN","type":"education","lineage":["https://openalex.org/I152269853"]},{"id":"https://openalex.org/I4210142748","display_name":"Shandong Academy of Sciences","ror":"https://ror.org/04y8d6y55","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210142748"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jia Yuan Zhu","raw_affiliation_strings":["Shandong Artificial Intelligence Institute, Qilu University of Technology (Shandong Academy of Sciences), China"],"affiliations":[{"raw_affiliation_string":"Shandong Artificial Intelligence Institute, Qilu University of Technology (Shandong Academy of Sciences), China","institution_ids":["https://openalex.org/I4210142748","https://openalex.org/I152269853"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103119961","display_name":"Hui Liu","orcid":"https://orcid.org/0009-0006-1623-4268"},"institutions":[{"id":"https://openalex.org/I4210142748","display_name":"Shandong Academy of Sciences","ror":"https://ror.org/04y8d6y55","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210142748"]},{"id":"https://openalex.org/I152269853","display_name":"Qilu University of Technology","ror":"https://ror.org/04hyzq608","country_code":"CN","type":"education","lineage":["https://openalex.org/I152269853"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hui Liu","raw_affiliation_strings":["Shandong Artificial Intelligence Institute, Qilu University of Technology (Shandong Academy of Sciences), China"],"affiliations":[{"raw_affiliation_string":"Shandong Artificial Intelligence Institute, Qilu University of Technology (Shandong Academy of Sciences), China","institution_ids":["https://openalex.org/I4210142748","https://openalex.org/I152269853"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5113133646","display_name":"Xiao Wei Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210142748","display_name":"Shandong Academy of Sciences","ror":"https://ror.org/04y8d6y55","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210142748"]},{"id":"https://openalex.org/I152269853","display_name":"Qilu University of Technology","ror":"https://ror.org/04hyzq608","country_code":"CN","type":"education","lineage":["https://openalex.org/I152269853"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiao Wei Liu","raw_affiliation_strings":["Qilu University of Technology (Shandong Academy of Sciences), China"],"affiliations":[{"raw_affiliation_string":"Qilu University of Technology (Shandong Academy of Sciences), China","institution_ids":["https://openalex.org/I152269853","https://openalex.org/I4210142748"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5064873899"],"corresponding_institution_ids":["https://openalex.org/I152269853","https://openalex.org/I4210142748"],"apc_list":null,"apc_paid":null,"fwci":0.5326,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.73521551,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"25","last_page":"28"},"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/T11021","display_name":"ECG Monitoring and Analysis","score":0.9962000250816345,"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/residual","display_name":"Residual","score":0.7352129817008972},{"id":"https://openalex.org/keywords/phonocardiogram","display_name":"Phonocardiogram","score":0.6830146908760071},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.664466917514801},{"id":"https://openalex.org/keywords/modal","display_name":"Modal","score":0.6426342129707336},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6389947533607483},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.608020544052124},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4618898928165436},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.46060803532600403},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4463815987110138},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.33426976203918457},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.11542898416519165}],"concepts":[{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.7352129817008972},{"id":"https://openalex.org/C159693508","wikidata":"https://www.wikidata.org/wiki/Q3301075","display_name":"Phonocardiogram","level":2,"score":0.6830146908760071},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.664466917514801},{"id":"https://openalex.org/C71139939","wikidata":"https://www.wikidata.org/wiki/Q910194","display_name":"Modal","level":2,"score":0.6426342129707336},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6389947533607483},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.608020544052124},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4618898928165436},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.46060803532600403},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4463815987110138},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33426976203918457},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.11542898416519165},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C188027245","wikidata":"https://www.wikidata.org/wiki/Q750446","display_name":"Polymer chemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3616901.3616907","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3616901.3616907","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 International Conference on Frontiers of Artificial Intelligence and Machine Learning","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being","score":0.46000000834465027}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W2557139718","https://openalex.org/W2593628220","https://openalex.org/W2886034601","https://openalex.org/W2888010882","https://openalex.org/W2953193031","https://openalex.org/W2971515944","https://openalex.org/W2977063526","https://openalex.org/W3021883627","https://openalex.org/W3035159033","https://openalex.org/W3039983345","https://openalex.org/W3119536276","https://openalex.org/W3132952621","https://openalex.org/W4293676981","https://openalex.org/W4304820719","https://openalex.org/W4311050759"],"related_works":["https://openalex.org/W2387004671","https://openalex.org/W2163299290","https://openalex.org/W2066804906","https://openalex.org/W2184139745","https://openalex.org/W2463659687","https://openalex.org/W1571173121","https://openalex.org/W4296634763","https://openalex.org/W2780321582","https://openalex.org/W3191203122","https://openalex.org/W3003241815"],"abstract_inverted_index":{"Cardiovascular":[0],"diseases":[1,60],"are":[2,32,149],"commonly":[3],"detected":[4],"using":[5,61,130,138],"bioelectrical":[6],"signals":[7,148],"such":[8],"as":[9,133,135],"electrocardiogram":[10],"(ECG)":[11],"and":[12,81,102,146],"phonocardiogram":[13],"(PCG),":[14],"which":[15,127],"reflect":[16],"the":[17,20,44,56,99,113],"state":[18],"of":[19,47,58,115,125],"heart":[21],"from":[22,79],"different":[23,92],"perspectives.":[24],"However,":[25],"previous":[26],"studies":[27,137],"on":[28,35],"cardiovascular":[29,59,152],"disease":[30,153],"detection":[31,57],"mainly":[33],"based":[34],"single-modal":[36,131],"data,":[37],"i.e.":[38],"ECG":[39,80,145],"or":[40],"PCG":[41,82,147],"alone.":[42],"With":[43],"fast":[45],"development":[46],"deep":[48,77],"learning,":[49],"researchers":[50],"begin":[51],"to":[52,55,97],"pay":[53],"attention":[54],"multi-modal":[62,139],"data.":[63,140],"In":[64],"this":[65],"study,":[66],"we":[67],"propose":[68],"a":[69],"multi-branch":[70],"residual":[71,85],"network":[72],"that":[73,112,144],"can":[74,88],"automatically":[75],"extract":[76,89],"features":[78,90,101],"signals.":[83],"Different":[84],"branches":[86],"(SE-ResNet)":[87],"at":[91],"scales.":[93],"We":[94],"use":[95],"PCA":[96],"select":[98],"fused":[100],"apply":[103],"SVM":[104],"classifier":[105],"for":[106],"classification.":[107],"The":[108],"experimental":[109],"results":[110],"demonstrate":[111],"accuracy":[114],"our":[116],"proposed":[117],"method":[118],"is":[119],"93.1%":[120],"with":[121],"an":[122],"AUC":[123],"value":[124],"0.967,":[126],"outperforms":[128],"methods":[129],"data":[132],"well":[134],"existing":[136],"Our":[141],"findings":[142],"confirm":[143],"complementary":[150],"in":[151],"detection.":[154]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-15T23:13:30.683059","created_date":"2025-10-10T00:00:00"}
