{"id":"https://openalex.org/W3011362036","doi":"https://doi.org/10.1109/ccwc47524.2020.9031126","title":"A Wavelet Compression based Multi-resolution Bidirectional LSTM Network for Electrocardiogram Biometric Classification in constructing Biometric Ontology","display_name":"A Wavelet Compression based Multi-resolution Bidirectional LSTM Network for Electrocardiogram Biometric Classification in constructing Biometric Ontology","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3011362036","doi":"https://doi.org/10.1109/ccwc47524.2020.9031126","mag":"3011362036"},"language":"en","primary_location":{"id":"doi:10.1109/ccwc47524.2020.9031126","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ccwc47524.2020.9031126","pdf_url":null,"source":{"id":"https://openalex.org/S4306498584","display_name":"2020 10th Annual Computing and Communication Workshop and Conference (CCWC)","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":"2020 10th Annual Computing and Communication Workshop and Conference (CCWC)","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/A5071393059","display_name":"Htet Myet Lynn","orcid":"https://orcid.org/0000-0003-1111-3337"},"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":"Htet Myet Lynn","raw_affiliation_strings":["Dept. of Computer Engineering, Chosun University, Gwangju, South Korea"],"affiliations":[{"raw_affiliation_string":"Dept. of Computer Engineering, Chosun University, Gwangju, South Korea","institution_ids":["https://openalex.org/I152238500"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079110192","display_name":"Taekeun Hong","orcid":"https://orcid.org/0000-0002-1189-4041"},"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":"Taekeun Hong","raw_affiliation_strings":["Dept. of Computer Engineering, Chosun University, Gwangju, South Korea"],"affiliations":[{"raw_affiliation_string":"Dept. of Computer Engineering, Chosun University, Gwangju, South Korea","institution_ids":["https://openalex.org/I152238500"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111479318","display_name":"Hyoungju Kim","orcid":null},"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":"Hyoungju Kim","raw_affiliation_strings":["Dept. of Computer Engineering, Chosun University, Gwangju, South Korea"],"affiliations":[{"raw_affiliation_string":"Dept. of Computer Engineering, Chosun University, Gwangju, South Korea","institution_ids":["https://openalex.org/I152238500"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100606106","display_name":"Sung Hwan Kim","orcid":"https://orcid.org/0000-0002-6888-774X"},"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":"Sung Hwan Kim","raw_affiliation_strings":["SW Convergence Education Institute, Chosun University, Gwangju, South Korea"],"affiliations":[{"raw_affiliation_string":"SW Convergence Education Institute, Chosun University, Gwangju, South Korea","institution_ids":["https://openalex.org/I152238500"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5061116975","display_name":"Pankoo Kim","orcid":"https://orcid.org/0000-0003-0111-5152"},"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":"Pankoo Kim","raw_affiliation_strings":["Dept. of Computer Engineering, Chosun University, Gwangju, South Korea"],"affiliations":[{"raw_affiliation_string":"Dept. of Computer Engineering, Chosun University, Gwangju, South Korea","institution_ids":["https://openalex.org/I152238500"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5071393059"],"corresponding_institution_ids":["https://openalex.org/I152238500"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.0826087,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":97},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11021","display_name":"ECG Monitoring and Analysis","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T11021","display_name":"ECG Monitoring and Analysis","score":0.9998999834060669,"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":0.9983999729156494,"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"}},{"id":"https://openalex.org/T11196","display_name":"Non-Invasive Vital Sign Monitoring","score":0.9890999794006348,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.85761559009552},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7053452134132385},{"id":"https://openalex.org/keywords/biometrics","display_name":"Biometrics","score":0.6693783402442932},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.6367208957672119},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.5458846092224121},{"id":"https://openalex.org/keywords/wavelet-transform","display_name":"Wavelet transform","score":0.4972379505634308},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.48000186681747437},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.47710829973220825},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.46464937925338745},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.364460289478302},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.30883660912513733}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.85761559009552},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7053452134132385},{"id":"https://openalex.org/C184297639","wikidata":"https://www.wikidata.org/wiki/Q177765","display_name":"Biometrics","level":2,"score":0.6693783402442932},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.6367208957672119},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.5458846092224121},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.4972379505634308},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.48000186681747437},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.47710829973220825},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.46464937925338745},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.364460289478302},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.30883660912513733}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ccwc47524.2020.9031126","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ccwc47524.2020.9031126","pdf_url":null,"source":{"id":"https://openalex.org/S4306498584","display_name":"2020 10th Annual Computing and Communication Workshop and Conference (CCWC)","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":"2020 10th Annual Computing and Communication Workshop and Conference (CCWC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4699999988079071,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"},{"score":0.4399999976158142,"display_name":"Partnerships for the goals","id":"https://metadata.un.org/sdg/17"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W176103723","https://openalex.org/W574370508","https://openalex.org/W1983903026","https://openalex.org/W2034365922","https://openalex.org/W2063923412","https://openalex.org/W2112796928","https://openalex.org/W2132984323","https://openalex.org/W2137409440","https://openalex.org/W2140920882","https://openalex.org/W2162273778","https://openalex.org/W2579158383","https://openalex.org/W2612184698","https://openalex.org/W2618530766","https://openalex.org/W2620050178","https://openalex.org/W2620908499","https://openalex.org/W2784158897","https://openalex.org/W2899992988","https://openalex.org/W2914320326","https://openalex.org/W2963478701","https://openalex.org/W4205574085","https://openalex.org/W4255272544","https://openalex.org/W4289304905","https://openalex.org/W6745068390","https://openalex.org/W6756147556","https://openalex.org/W6805917213"],"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/W4241440711","https://openalex.org/W3008584592","https://openalex.org/W2077021924"],"abstract_inverted_index":{"In":[0,94],"this":[1],"study,":[2],"we":[3],"explore":[4],"the":[5,12,70,80,83,102,106,116,137,139,163],"use":[6],"of":[7,14,63,85,101,109,118,155,165],"deep":[8],"learning":[9,27,119,173],"approaches":[10],"for":[11,18,54,74,175],"task":[13,56],"classifying":[15],"electrocardiogram(ECG)":[16],"recordings":[17],"biometric":[19,24],"human":[20],"identification":[21,55],"in":[22,57,82,87,150,153],"developing":[23],"ontology.":[25,93],"Deep":[26],"techniques":[28],"such":[29],"as":[30],"traditional":[31],"recurrent":[32],"neural":[33],"network":[34,51],"with":[35,43],"long-short":[36],"term":[37],"memory":[38],"unit":[39],"gate":[40],"(RNN-LSTM),":[41],"RNN":[42,124,143],"GRU":[44],"cell":[45],"unit(RNN-GRU),":[46],"and":[47,72,77,113,146,157,169],"multi-resolution":[48,170],"bidirectional":[49,171],"LSTM":[50,172],"are":[52],"applied":[53,131],"terms":[58,154],"non-fiducial":[59,75],"approach.":[60],"The":[61],"idea":[62],"investigating":[64],"proposed":[65,140],"architectures":[66],"is":[67,130],"to":[68,78,89,104,114,122,132],"study":[69],"accuracy":[71,156],"performance":[73,117,152],"approach,":[76],"expand":[79],"concepts":[81],"domain":[84],"biometrics":[86],"order":[88],"build":[90],"more":[91],"precise":[92],"addition,":[95],"By":[96],"applying":[97,166],"wavelet":[98,127,167],"compression":[99,128,168],"version":[100],"data,":[103],"enrich":[105],"time-frequency":[107],"representation":[108],"original":[110],"ECG":[111,134],"signal,":[112],"increase":[115],"procedure":[120],"compared":[121],"other":[123],"based":[125,144],"methods,":[126],"technique":[129],"normalized":[133],"signals.":[135],"From":[136],"experiment,":[138],"method":[141,174],"surpassed":[142],"networks":[145],"recent":[147],"state-of-the-art":[148],"studies":[149],"generalization":[151],"F1":[158],"score.":[159],"This":[160],"paper":[161],"demonstrates":[162],"effectiveness":[164],"a":[176],"high":[177],"classification":[178],"capability.":[179]},"counts_by_year":[{"year":2024,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
