{"id":"https://openalex.org/W4392152823","doi":"https://doi.org/10.1109/globecom54140.2023.10436801","title":"Improving Heart Rate Range Classification Using Doppler Radar with GAN-based Data Augmentation","display_name":"Improving Heart Rate Range Classification Using Doppler Radar with GAN-based Data Augmentation","publication_year":2023,"publication_date":"2023-12-04","ids":{"openalex":"https://openalex.org/W4392152823","doi":"https://doi.org/10.1109/globecom54140.2023.10436801"},"language":"en","primary_location":{"id":"doi:10.1109/globecom54140.2023.10436801","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globecom54140.2023.10436801","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"GLOBECOM 2023 - 2023 IEEE Global Communications Conference","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/A5100585216","display_name":"Danyuan Yu","orcid":null},"institutions":[{"id":"https://openalex.org/I203951103","display_name":"Keio University","ror":"https://ror.org/02kn6nx58","country_code":"JP","type":"education","lineage":["https://openalex.org/I203951103"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Danyuan Yu","raw_affiliation_strings":["Graduate School of Science and Technology, Keio University,Yokohama,Japan","Graduate School of Science and Technology, Keio University, Yokohama, Japan"],"affiliations":[{"raw_affiliation_string":"Graduate School of Science and Technology, Keio University,Yokohama,Japan","institution_ids":["https://openalex.org/I203951103"]},{"raw_affiliation_string":"Graduate School of Science and Technology, Keio University, Yokohama, Japan","institution_ids":["https://openalex.org/I203951103"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068994330","display_name":"Mondher Bouazizi","orcid":"https://orcid.org/0000-0001-7055-9318"},"institutions":[{"id":"https://openalex.org/I203951103","display_name":"Keio University","ror":"https://ror.org/02kn6nx58","country_code":"JP","type":"education","lineage":["https://openalex.org/I203951103"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Mondher Bouazizi","raw_affiliation_strings":["Keio University,Department of Information and Computer Science,Yokohama,Japan","Department of Information and Computer Science, Keio University, Yokohama, Japan"],"affiliations":[{"raw_affiliation_string":"Keio University,Department of Information and Computer Science,Yokohama,Japan","institution_ids":["https://openalex.org/I203951103"]},{"raw_affiliation_string":"Department of Information and Computer Science, Keio University, Yokohama, Japan","institution_ids":["https://openalex.org/I203951103"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5094001515","display_name":"Tomoaki Ohtsukil","orcid":null},"institutions":[{"id":"https://openalex.org/I203951103","display_name":"Keio University","ror":"https://ror.org/02kn6nx58","country_code":"JP","type":"education","lineage":["https://openalex.org/I203951103"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Tomoaki Ohtsukil","raw_affiliation_strings":["Keio University,Department of Information and Computer Science,Yokohama,Japan","Department of Information and Computer Science, Keio University, Yokohama, Japan"],"affiliations":[{"raw_affiliation_string":"Keio University,Department of Information and Computer Science,Yokohama,Japan","institution_ids":["https://openalex.org/I203951103"]},{"raw_affiliation_string":"Department of Information and Computer Science, Keio University, Yokohama, Japan","institution_ids":["https://openalex.org/I203951103"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100585216"],"corresponding_institution_ids":["https://openalex.org/I203951103"],"apc_list":null,"apc_paid":null,"fwci":0.3375,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.55330226,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"3885","last_page":"3890"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11196","display_name":"Non-Invasive Vital Sign Monitoring","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9958000183105469,"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/T10745","display_name":"Heart Rate Variability and Autonomic Control","score":0.9922000169754028,"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/doppler-radar","display_name":"Doppler radar","score":0.6917593479156494},{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.61868816614151},{"id":"https://openalex.org/keywords/doppler-effect","display_name":"Doppler effect","score":0.5558950901031494},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.5434451699256897},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5205700993537903},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.4063863754272461},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.27131372690200806},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.18835806846618652},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.16026923060417175},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.091275155544281}],"concepts":[{"id":"https://openalex.org/C2778559676","wikidata":"https://www.wikidata.org/wiki/Q1334213","display_name":"Doppler radar","level":3,"score":0.6917593479156494},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.61868816614151},{"id":"https://openalex.org/C142757262","wikidata":"https://www.wikidata.org/wiki/Q76436","display_name":"Doppler effect","level":2,"score":0.5558950901031494},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.5434451699256897},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5205700993537903},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.4063863754272461},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.27131372690200806},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.18835806846618652},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.16026923060417175},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.091275155544281},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/globecom54140.2023.10436801","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globecom54140.2023.10436801","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"GLOBECOM 2023 - 2023 IEEE Global Communications Conference","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5699999928474426,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W147281455","https://openalex.org/W2798122215","https://openalex.org/W2891342985","https://openalex.org/W2896655658","https://openalex.org/W2898845453","https://openalex.org/W2904022596","https://openalex.org/W2963073614","https://openalex.org/W2963684088","https://openalex.org/W2969771517","https://openalex.org/W3007969083","https://openalex.org/W3015418729","https://openalex.org/W3042995237","https://openalex.org/W3084352865","https://openalex.org/W3089350906","https://openalex.org/W3096831136","https://openalex.org/W3123326053","https://openalex.org/W3127284993","https://openalex.org/W3157843597","https://openalex.org/W4200056512","https://openalex.org/W4200157809","https://openalex.org/W4200489229","https://openalex.org/W4295521014","https://openalex.org/W4385245566","https://openalex.org/W6685352114","https://openalex.org/W6735913928","https://openalex.org/W6750469568","https://openalex.org/W6841355472"],"related_works":["https://openalex.org/W1978025251","https://openalex.org/W2361241744","https://openalex.org/W2086314607","https://openalex.org/W2235285389","https://openalex.org/W4252012301","https://openalex.org/W2581209175","https://openalex.org/W2532305960","https://openalex.org/W2900664875","https://openalex.org/W2022955050","https://openalex.org/W2944876982"],"abstract_inverted_index":{"In":[0,137,161],"this":[1],"paper,":[2],"we":[3,31],"propose":[4],"a":[5,33,74,152],"novel":[6],"data":[7,172],"augmentation":[8,76],"framework":[9],"where":[10],"an":[11],"attention-based":[12],"Wasserstein":[13],"Generative":[14],"Adversarial":[15],"Networks-Gradient":[16],"penalty":[17],"(aWGAN-GP)":[18],"is":[19,71,81],"used":[20,95,106],"to":[21,47,83,96,101,163],"augment":[22],"Doppler":[23,49,98,114,158],"radar":[24,50,99,115,159],"signals":[25,51,80,100,116],"from":[26],"electrocardiogram":[27],"(ECG)":[28],"signals.":[29,160],"Besides,":[30],"present":[32],"new":[34,85],"heart":[35],"rate":[36],"(HR)":[37],"range":[38,165],"classi-fication":[39],"algorithm":[40],"by":[41,107,118],"applying":[42],"support":[43],"vector":[44],"machine":[45],"(SVM)":[46],"classify":[48],"depending":[52],"on":[53],"the":[54,65,89,103,113,139,143,156,167,171],"HR":[55,164],"ranges":[56],"(i.e.,":[57],"into":[58],"low,":[59],"normal,":[60],"and":[61,132,175,185],"high).":[62],"Finally,":[63],"since":[64],"class":[66],"distribution":[67],"of":[68,126,182],"ECG":[69,79,86],"samples":[70],"also":[72],"imbalanced,":[73],"simple":[75],"method":[77,141,169],"for":[78,88],"proposed":[82,140,168],"prepare":[84],"inputs":[87],"trained":[90],"generator,":[91],"which":[92,150],"can":[93],"be":[94],"synthesize":[97],"balance":[102],"training":[104],"set":[105],"SVM.":[108],"Our":[109],"experiments":[110],"show":[111],"that":[112],"generated":[117,157],"aWGAN-GP":[119],"provide":[120],"more":[121],"accurate":[122],"morphology":[123],"in":[124,155,180],"terms":[125,181],"root":[127],"mean":[128],"squared":[129],"error":[130],"(RMSE)":[131],"Pearson's":[133],"correlation":[134],"coefficient":[135],"(PCC).":[136],"addition,":[138],"achieves":[142],"lowest":[144],"relative":[145],"Fr\u00e9chet":[146],"inception":[147],"distance":[148],"(rFID),":[149],"shows":[151],"better":[153,177],"diversity":[154],"regard":[162],"classification,":[166],"relieves":[170],"imbalance":[173],"problem":[174],"obtains":[176],"classification":[178],"performance":[179],"accuracy,":[183],"F1-score,":[184],"recall.":[186]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-25T21:42:39.735039","created_date":"2025-10-10T00:00:00"}