{"id":"https://openalex.org/W4399658805","doi":"https://doi.org/10.1145/3641584.3641632","title":"Multimodal 1D-to-2D Signal Transformation and Pulse disease Recognition","display_name":"Multimodal 1D-to-2D Signal Transformation and Pulse disease Recognition","publication_year":2023,"publication_date":"2023-09-22","ids":{"openalex":"https://openalex.org/W4399658805","doi":"https://doi.org/10.1145/3641584.3641632"},"language":"en","primary_location":{"id":"doi:10.1145/3641584.3641632","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3641584.3641632","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 6th International Conference on Artificial Intelligence and Pattern Recognition (AIPR)","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/A5071018114","display_name":"L. Fan","orcid":"https://orcid.org/0009-0008-5760-061X"},"institutions":[{"id":"https://openalex.org/I4210136859","display_name":"Xi\u2019an University of Posts and Telecommunications","ror":"https://ror.org/04jn0td46","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136859"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Lin Fan","raw_affiliation_strings":["Xi'an University of Posts and Telecommunications, China"],"affiliations":[{"raw_affiliation_string":"Xi'an University of Posts and Telecommunications, China","institution_ids":["https://openalex.org/I4210136859"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5099124713","display_name":"Bochen Dang","orcid":"https://orcid.org/0000-0002-7735-8436"},"institutions":[{"id":"https://openalex.org/I4210136859","display_name":"Xi\u2019an University of Posts and Telecommunications","ror":"https://ror.org/04jn0td46","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136859"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bochen Dang","raw_affiliation_strings":["Xi'an University of Posts and Telecommunications, China"],"affiliations":[{"raw_affiliation_string":"Xi'an University of Posts and Telecommunications, China","institution_ids":["https://openalex.org/I4210136859"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100751424","display_name":"Zhongmin Wang","orcid":"https://orcid.org/0000-0003-0870-454X"},"institutions":[{"id":"https://openalex.org/I4210136859","display_name":"Xi\u2019an University of Posts and Telecommunications","ror":"https://ror.org/04jn0td46","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136859"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhongmin Wang","raw_affiliation_strings":["Xi'an University of Posts and Telecommunications, China"],"affiliations":[{"raw_affiliation_string":"Xi'an University of Posts and Telecommunications, China","institution_ids":["https://openalex.org/I4210136859"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5079504151","display_name":"Rong Gui Zhang","orcid":"https://orcid.org/0009-0008-3048-0750"},"institutions":[{"id":"https://openalex.org/I4210136859","display_name":"Xi\u2019an University of Posts and Telecommunications","ror":"https://ror.org/04jn0td46","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136859"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rong Zhang","raw_affiliation_strings":["Xi'an University of Posts and Telecommunications, China"],"affiliations":[{"raw_affiliation_string":"Xi'an University of Posts and Telecommunications, China","institution_ids":["https://openalex.org/I4210136859"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5071018114"],"corresponding_institution_ids":["https://openalex.org/I4210136859"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.20786176,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"324","last_page":"330"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11196","display_name":"Non-Invasive Vital Sign Monitoring","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11196","display_name":"Non-Invasive Vital Sign Monitoring","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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":0.9943000078201294,"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"}},{"id":"https://openalex.org/T11324","display_name":"Spectroscopy Techniques in Biomedical and Chemical Research","score":0.9879999756813049,"subfield":{"id":"https://openalex.org/subfields/1304","display_name":"Biophysics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.591062068939209},{"id":"https://openalex.org/keywords/transformation","display_name":"Transformation (genetics)","score":0.5833498239517212},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.49018245935440063},{"id":"https://openalex.org/keywords/pulse","display_name":"Pulse (music)","score":0.41670486330986023},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4166225790977478},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.40759381651878357},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.34497469663619995},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.1439639925956726}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.591062068939209},{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.5833498239517212},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.49018245935440063},{"id":"https://openalex.org/C2780167933","wikidata":"https://www.wikidata.org/wiki/Q1550652","display_name":"Pulse (music)","level":3,"score":0.41670486330986023},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4166225790977478},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.40759381651878357},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34497469663619995},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.1439639925956726},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3641584.3641632","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3641584.3641632","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 6th International Conference on Artificial Intelligence and Pattern Recognition (AIPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W2003321679","https://openalex.org/W2036745273","https://openalex.org/W2125702111","https://openalex.org/W2147800946","https://openalex.org/W2155273149","https://openalex.org/W2156703793","https://openalex.org/W2624439409","https://openalex.org/W2790486054","https://openalex.org/W2885329266","https://openalex.org/W2974952570","https://openalex.org/W2993627123","https://openalex.org/W3015287909","https://openalex.org/W3104186025","https://openalex.org/W3208133533","https://openalex.org/W4206280724","https://openalex.org/W6600388300","https://openalex.org/W6664747556"],"related_works":["https://openalex.org/W2353644209","https://openalex.org/W2593663830","https://openalex.org/W2348743188","https://openalex.org/W2902317490","https://openalex.org/W3014038144","https://openalex.org/W4387423451","https://openalex.org/W2356110154","https://openalex.org/W2025460258","https://openalex.org/W4297215628","https://openalex.org/W2377378424"],"abstract_inverted_index":{"Pulse":[0],"signals,":[1],"vital":[2],"indicators":[3],"of":[4,28,127,150,156],"intrinsic":[5],"health":[6],"information,":[7],"hold":[8],"diverse":[9],"applications":[10],"in":[11,15,35,167],"computer-aided":[12],"pulse":[13,22,114,168],"diagnosis":[14],"traditional":[16],"Chinese":[17],"medicine.":[18],"However,":[19],"conventional":[20],"one-dimensional":[21,55],"disease":[23,133,169],"recognition":[24],"methods":[25,52],"face":[26],"challenges":[27],"resemblance,":[29],"ambiguity,":[30],"low":[31],"accuracy,":[32],"and":[33,83,88,97,121,140,159],"instability":[34],"identifying":[36],"multiple":[37,51,107],"diseases.":[38],"To":[39],"address":[40],"these":[41],"issues,":[42],"this":[43],"study":[44],"presents":[45],"a":[46,113],"novel":[47],"approach":[48,145],"that":[49,125],"uses":[50],"to":[53,105,152],"transform":[54],"signals":[56,62,69],"into":[57,65],"two-dimensional":[58],"representations.":[59],"Initially,":[60],"original":[61],"are":[63,92],"segmented":[64],"periods,":[66],"treating":[67],"single-period":[68],"as":[70,109],"samples.":[71],"Then,":[72],"employing":[73],"short-time":[74],"Fourier":[75],"transform,":[76],"Gram":[77],"angle":[78],"field,":[79,82],"Markov":[80],"variation":[81],"recurrence":[84],"map,":[85],"2D":[86],"matrices,":[87],"corresponding":[89],"image":[90],"representations":[91],"derived":[93],"across":[94],"time,":[95],"frequency,":[96],"time-frequency":[98],"domains.":[99],"An":[100],"integrated":[101,161],"model":[102],"is":[103],"established":[104],"classify":[106],"images":[108],"features.":[110],"Experiments":[111],"utilize":[112],"dataset":[115],"from":[116],"211":[117],"hospitals":[118],"for":[119],"training":[120],"testing.":[122],"Results":[123],"reveal":[124],"devoid":[126],"manual":[128],"preprocessing,":[129],"signal":[130,142,157],"upscaling":[131,158],"enhances":[132],"classification":[134],"accuracy":[135,149],"by":[136],"capturing":[137],"both":[138],"temporal":[139],"periodic/non-periodic":[141],"information.":[143],"This":[144],"achieves":[146],"an":[147,160],"average":[148],"up":[151],"92%.":[153],"The":[154],"synergy":[155],"CNN":[162],"classifier":[163],"demonstrates":[164],"promising":[165],"potential":[166],"recognition.":[170]},"counts_by_year":[],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
