{"id":"https://openalex.org/W4320024301","doi":"https://doi.org/10.1109/bigdata55660.2022.10020601","title":"Performance Comparison for Deep Learning-Based Personal Identification from the database of ECG and EMG singals","display_name":"Performance Comparison for Deep Learning-Based Personal Identification from the database of ECG and EMG singals","publication_year":2022,"publication_date":"2022-12-17","ids":{"openalex":"https://openalex.org/W4320024301","doi":"https://doi.org/10.1109/bigdata55660.2022.10020601"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata55660.2022.10020601","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/bigdata55660.2022.10020601","pdf_url":null,"source":{"id":"https://openalex.org/S4363607709","display_name":"2022 IEEE International Conference on Big Data (Big Data)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Big Data (Big Data)","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/A5007040634","display_name":"Yeong-Hyeon Byeon","orcid":"https://orcid.org/0000-0002-7480-4971"},"institutions":[{"id":"https://openalex.org/I152238500","display_name":"Chosun University","ror":"https://ror.org/01zt9a375","country_code":"KR","type":"education","lineage":["https://openalex.org/I152238500"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Yeong-Hyeon Byeon","raw_affiliation_strings":["Chosun University,Dept. of Electronics Engineering,Gwangju,Korea","Dept. of Electronics Engineering, Chosun University, Gwangju, Korea"],"affiliations":[{"raw_affiliation_string":"Chosun University,Dept. of Electronics Engineering,Gwangju,Korea","institution_ids":["https://openalex.org/I152238500"]},{"raw_affiliation_string":"Dept. of Electronics Engineering, Chosun University, Gwangju, Korea","institution_ids":["https://openalex.org/I152238500"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5080268629","display_name":"Keun-Chang Kwak","orcid":"https://orcid.org/0000-0002-3821-0711"},"institutions":[{"id":"https://openalex.org/I152238500","display_name":"Chosun University","ror":"https://ror.org/01zt9a375","country_code":"KR","type":"education","lineage":["https://openalex.org/I152238500"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Keun-Chang Kwak","raw_affiliation_strings":["Chosun University,Dept. of Electronics Engineering,Gwangju,Korea","Dept. of Electronics Engineering, Chosun University, Gwangju, Korea"],"affiliations":[{"raw_affiliation_string":"Chosun University,Dept. of Electronics Engineering,Gwangju,Korea","institution_ids":["https://openalex.org/I152238500"]},{"raw_affiliation_string":"Dept. of Electronics Engineering, Chosun University, Gwangju, Korea","institution_ids":["https://openalex.org/I152238500"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5007040634"],"corresponding_institution_ids":["https://openalex.org/I152238500"],"apc_list":null,"apc_paid":null,"fwci":0.3206,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.5556729,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"54","issue":null,"first_page":"6733","last_page":"6735"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10784","display_name":"Muscle activation and electromyography studies","score":0.9994999766349792,"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/T10784","display_name":"Muscle activation and electromyography studies","score":0.9994999766349792,"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":0.9979000091552734,"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/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9973999857902527,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/biometrics","display_name":"Biometrics","score":0.8259460926055908},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7622212171554565},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6853251457214355},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6638807058334351},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.658221423625946},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6470857262611389},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.5420210957527161},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.5308208465576172},{"id":"https://openalex.org/keywords/wavelet-transform","display_name":"Wavelet transform","score":0.5182002186775208},{"id":"https://openalex.org/keywords/electromyography","display_name":"Electromyography","score":0.4709274172782898},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.4523889720439911},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.424325555562973}],"concepts":[{"id":"https://openalex.org/C184297639","wikidata":"https://www.wikidata.org/wiki/Q177765","display_name":"Biometrics","level":2,"score":0.8259460926055908},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7622212171554565},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6853251457214355},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6638807058334351},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.658221423625946},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6470857262611389},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.5420210957527161},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.5308208465576172},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.5182002186775208},{"id":"https://openalex.org/C2777515770","wikidata":"https://www.wikidata.org/wiki/Q507369","display_name":"Electromyography","level":2,"score":0.4709274172782898},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.4523889720439911},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.424325555562973},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C118552586","wikidata":"https://www.wikidata.org/wiki/Q7867","display_name":"Psychiatry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata55660.2022.10020601","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/bigdata55660.2022.10020601","pdf_url":null,"source":{"id":"https://openalex.org/S4363607709","display_name":"2022 IEEE International Conference on Big Data (Big Data)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320311687","display_name":"Ministry of Education","ror":"https://ror.org/03m01yf64"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W1940872118","https://openalex.org/W1984150290","https://openalex.org/W2123167643","https://openalex.org/W2620050178","https://openalex.org/W2766867718","https://openalex.org/W2767640440","https://openalex.org/W2779797561","https://openalex.org/W2968253494","https://openalex.org/W3041894881","https://openalex.org/W3120367727","https://openalex.org/W6640362995","https://openalex.org/W6767111920"],"related_works":["https://openalex.org/W2076845124","https://openalex.org/W2183964146","https://openalex.org/W2062586268","https://openalex.org/W2379932303","https://openalex.org/W4300873085","https://openalex.org/W2019582947","https://openalex.org/W3147744369","https://openalex.org/W2376139493","https://openalex.org/W3212688212","https://openalex.org/W2077021924"],"abstract_inverted_index":{"This":[0],"paper":[1],"is":[2,174],"concerned":[3],"with":[4,197],"performance":[5,120,202],"comparison":[6,121,196,203],"for":[7,38,204],"personal":[8,205],"identification":[9],"from":[10,122,135,138,148,151],"Electromyography":[11],"(EMG)":[12],"and":[13,26,105,126,141,163],"Electrocardiogram":[14],"(ECG)":[15],"signals.":[16],"For":[17],"this":[18],"purpose,":[19],"we":[20,117],"use":[21],"Convolutional":[22],"Neural":[23],"Network":[24],"(CNN)":[25],"Long":[27],"Short":[28],"Term":[29],"Memory":[30],"(LSTM)":[31],"as":[32,64,79,113],"the":[33,42,46,70,74,80,85,90,95,100,106,119,142,186,201],"representative":[34],"deep":[35],"learning":[36],"model":[37],"biometrics,":[39],"respectively.":[40],"In":[41],"case":[43,71],"of":[44,66,72,82,87,92,97,102,115],"CNN,":[45],"signals":[47,155,165,188,199],"are":[48,62,76,111,156,161,166],"transformed":[49,59],"into":[50],"two-dimensional":[51],"time-frequency":[52],"representation":[53],"based":[54],"on":[55],"wavelet":[56],"analysis.":[57],"The":[58,130,181],"2D":[60],"images":[61],"used":[63,112],"input":[65,114],"CNN.":[67],"Meanwhile,":[68],"in":[69,89,168,195],"LSTM,":[73],"features":[75],"extracted":[77],"such":[78],"mean":[81,91],"absolute":[83,93],"values,":[84,94],"amount":[86],"change":[88],"number":[96,101],"zero":[98],"crossings,":[99],"gradient":[103],"changes,":[104],"wavelength.":[107],"These":[108],"five":[109],"factors":[110],"LSTM.Finally,":[116],"perform":[118],"self-constructed":[123],"ECG":[124,131,164,187],"database":[125],"benchmark":[127],"EMG":[128,144,154,198],"database.":[129],"dataset":[132,145],"includes":[133,146],"data":[134,147],"100":[136],"people":[137,150],"1":[139],"channel":[140],"public":[143],"50":[149],"8":[152],"channels.":[153],"obtained":[157,167],"while":[158],"some":[159],"motions":[160],"performing":[162],"a":[169],"rest":[170],"state.":[171],"ECG-based":[172],"biometrics":[173],"10.95%":[175],"higher":[176],"accuracy":[177],"than":[178],"EMG-based":[179],"biometrics.":[180],"experimental":[182],"results":[183],"revealed":[184],"that":[185],"showed":[189],"more":[190],"effective":[191],"individual\u2019s":[192],"unique":[193],"information":[194],"through":[200],"identification.":[206]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2025-12-24T23:09:58.560324","created_date":"2025-10-10T00:00:00"}
