{"id":"https://openalex.org/W4280502099","doi":"https://doi.org/10.1109/tbme.2022.3174509","title":"A Deep Convolutional Autoencoder for Automatic Motion Artifact Removal in Electrodermal Activity","display_name":"A Deep Convolutional Autoencoder for Automatic Motion Artifact Removal in Electrodermal Activity","publication_year":2022,"publication_date":"2022-05-11","ids":{"openalex":"https://openalex.org/W4280502099","doi":"https://doi.org/10.1109/tbme.2022.3174509","pmid":"https://pubmed.ncbi.nlm.nih.gov/35544485"},"language":"en","primary_location":{"id":"doi:10.1109/tbme.2022.3174509","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tbme.2022.3174509","pdf_url":null,"source":{"id":"https://openalex.org/S5240358","display_name":"IEEE Transactions on Biomedical Engineering","issn_l":"0018-9294","issn":["0018-9294","1558-2531"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Biomedical Engineering","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5056148336","display_name":"Md-Billal Hossain","orcid":"https://orcid.org/0000-0002-2344-8321"},"institutions":[{"id":"https://openalex.org/I140172145","display_name":"University of Connecticut","ror":"https://ror.org/02der9h97","country_code":"US","type":"education","lineage":["https://openalex.org/I140172145"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Md-Billal Hossain","raw_affiliation_strings":["Department of Biomedical Engineering, University of Connecticut, USA"],"raw_orcid":"https://orcid.org/0000-0002-2344-8321","affiliations":[{"raw_affiliation_string":"Department of Biomedical Engineering, University of Connecticut, USA","institution_ids":["https://openalex.org/I140172145"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012375687","display_name":"Hugo F. Posada\u2013Quintero","orcid":"https://orcid.org/0000-0003-4514-4772"},"institutions":[{"id":"https://openalex.org/I140172145","display_name":"University of Connecticut","ror":"https://ror.org/02der9h97","country_code":"US","type":"education","lineage":["https://openalex.org/I140172145"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hugo F. Posada-Quintero","raw_affiliation_strings":["Department of Biomedical Engineering, University of Connecticut, USA"],"raw_orcid":"https://orcid.org/0000-0003-4514-4772","affiliations":[{"raw_affiliation_string":"Department of Biomedical Engineering, University of Connecticut, USA","institution_ids":["https://openalex.org/I140172145"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5032573836","display_name":"Ki H. Chon","orcid":"https://orcid.org/0000-0002-4422-4837"},"institutions":[{"id":"https://openalex.org/I140172145","display_name":"University of Connecticut","ror":"https://ror.org/02der9h97","country_code":"US","type":"education","lineage":["https://openalex.org/I140172145"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ki H. Chon","raw_affiliation_strings":["Department of Biomedical Engineering, University of Connecticut, Storrs, CT, USA"],"raw_orcid":"https://orcid.org/0000-0002-4422-4837","affiliations":[{"raw_affiliation_string":"Department of Biomedical Engineering, University of Connecticut, Storrs, CT, USA","institution_ids":["https://openalex.org/I140172145"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I140172145"],"apc_list":null,"apc_paid":null,"fwci":2.7218,"has_fulltext":false,"cited_by_count":24,"citation_normalized_percentile":{"value":0.90569764,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":"69","issue":"12","first_page":"3601","last_page":"3611"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.6747999787330627,"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"}},"topics":[{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.6747999787330627,"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/T11601","display_name":"Neuroscience and Neural Engineering","score":0.08640000224113464,"subfield":{"id":"https://openalex.org/subfields/2804","display_name":"Cellular and Molecular 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/T10667","display_name":"Emotion and Mood Recognition","score":0.04800000041723251,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.719153881072998},{"id":"https://openalex.org/keywords/artifact","display_name":"Artifact (error)","score":0.6948056221008301},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.6592552065849304},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6229605674743652},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6022722125053406},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.517561137676239},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.48037371039390564},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.4546128511428833},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.3461194634437561},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.3213077783584595},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.260697603225708},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.23125749826431274}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.719153881072998},{"id":"https://openalex.org/C2779010991","wikidata":"https://www.wikidata.org/wiki/Q2720909","display_name":"Artifact (error)","level":2,"score":0.6948056221008301},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.6592552065849304},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6229605674743652},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6022722125053406},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.517561137676239},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.48037371039390564},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.4546128511428833},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3461194634437561},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3213077783584595},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.260697603225708},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.23125749826431274},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D005712","descriptor_name":"Galvanic Skin Response","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D005712","descriptor_name":"Galvanic Skin Response","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D005712","descriptor_name":"Galvanic Skin Response","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D009038","descriptor_name":"Motion","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D009038","descriptor_name":"Motion","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D009038","descriptor_name":"Motion","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016477","descriptor_name":"Artifacts","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016477","descriptor_name":"Artifacts","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016477","descriptor_name":"Artifacts","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D059629","descriptor_name":"Signal-To-Noise Ratio","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D059629","descriptor_name":"Signal-To-Noise Ratio","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D059629","descriptor_name":"Signal-To-Noise Ratio","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false}],"locations_count":2,"locations":[{"id":"doi:10.1109/tbme.2022.3174509","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tbme.2022.3174509","pdf_url":null,"source":{"id":"https://openalex.org/S5240358","display_name":"IEEE Transactions on Biomedical Engineering","issn_l":"0018-9294","issn":["0018-9294","1558-2531"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Biomedical Engineering","raw_type":"journal-article"},{"id":"pmid:35544485","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/35544485","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on bio-medical engineering","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5575348135","display_name":null,"funder_award_id":"N00014-21-1-2255","funder_id":"https://openalex.org/F4320337345","funder_display_name":"Office of Naval Research"}],"funders":[{"id":"https://openalex.org/F4320337345","display_name":"Office of Naval Research","ror":"https://ror.org/00rk2pe57"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":70,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1973681148","https://openalex.org/W2025768430","https://openalex.org/W2062914254","https://openalex.org/W2078696200","https://openalex.org/W2105036571","https://openalex.org/W2113278123","https://openalex.org/W2122538988","https://openalex.org/W2145287260","https://openalex.org/W2157570848","https://openalex.org/W2177066871","https://openalex.org/W2294683276","https://openalex.org/W2295748404","https://openalex.org/W2296581541","https://openalex.org/W2405774341","https://openalex.org/W2471801048","https://openalex.org/W2504069552","https://openalex.org/W2520289546","https://openalex.org/W2605376995","https://openalex.org/W2610751394","https://openalex.org/W2764271683","https://openalex.org/W2791179640","https://openalex.org/W2794997027","https://openalex.org/W2889729260","https://openalex.org/W2904680839","https://openalex.org/W2909512602","https://openalex.org/W2945801048","https://openalex.org/W2952116256","https://openalex.org/W2953127026","https://openalex.org/W2980628905","https://openalex.org/W2997574889","https://openalex.org/W3000402017","https://openalex.org/W3016291932","https://openalex.org/W3017298754","https://openalex.org/W3049360655","https://openalex.org/W3082906260","https://openalex.org/W3109377843","https://openalex.org/W3209540137","https://openalex.org/W3210622964","https://openalex.org/W3210949351","https://openalex.org/W3211175859","https://openalex.org/W3212331003","https://openalex.org/W3213976808","https://openalex.org/W4200245573","https://openalex.org/W4200335619","https://openalex.org/W4200367885","https://openalex.org/W4200499555","https://openalex.org/W4205185431","https://openalex.org/W4206194712","https://openalex.org/W4220874301","https://openalex.org/W4225607561","https://openalex.org/W4236477222","https://openalex.org/W4288076474","https://openalex.org/W4294567867","https://openalex.org/W4294975385","https://openalex.org/W4295312788","https://openalex.org/W4298441315","https://openalex.org/W4393552193","https://openalex.org/W4393566964","https://openalex.org/W4393654451","https://openalex.org/W4393665771","https://openalex.org/W4393689093","https://openalex.org/W4393708402","https://openalex.org/W4393824642","https://openalex.org/W4393892718","https://openalex.org/W6631190155","https://openalex.org/W6681096077","https://openalex.org/W6720275808","https://openalex.org/W6734035190","https://openalex.org/W6766978945"],"related_works":["https://openalex.org/W3013693939","https://openalex.org/W2159052453","https://openalex.org/W2566616303","https://openalex.org/W3131327266","https://openalex.org/W4297051394","https://openalex.org/W2752972570","https://openalex.org/W2734887215","https://openalex.org/W2803255133","https://openalex.org/W2669956259","https://openalex.org/W4249005693"],"abstract_inverted_index":{"OBJECTIVE:":[0],"This":[1],"study":[2],"aimed":[3],"to":[4,56,236],"develop":[5],"a":[6,25,51,88,172,201,238],"robust":[7],"and":[8,70,81,97,112,124,134,154],"data":[9,84,190,199],"driven":[10],"automatic":[11,32],"motion":[12,118],"artifacts":[13],"(MA)":[14],"removal":[15,34],"technique":[16],"from":[17,166,191],"electrodermal":[18],"activity":[19],"(EDA)":[20],"signal.":[21],"METHODS:":[22],"we":[23],"proposed":[24],"deep":[26],"convolutional":[27],"autoencoder":[28],"(DCAE)":[29],"approach":[30],"for":[31],"MA":[33,83],"in":[35,91,211],"EDA":[36,58,244],"signals.":[37],"Our":[38],"model":[39,104,232],"was":[40,63],"trained":[41,69],"using":[42,50,75,87],"several":[43],"publicly":[44],"available":[45],"datasets":[46,114],"that":[47,144,215],"were":[48],"collected":[49,86],"wide":[52],"variety":[53],"of":[54,101,145,176,204,241],"stimuli":[55],"cause":[57],"reactions;":[59],"the":[60,99,106,146,159,163,167,187,192,196,212,225],"sample":[61],"size":[62],"large":[64],"([Formula:":[65],"see":[66,152],"text]).":[67],"We":[68,94],"validated":[71],"our":[72,92,102,216],"DCAE":[73,103,217,231],"network":[74],"both":[76],"Gaussian":[77],"white":[78],"noise":[79],"(GWN)":[80],"realistic":[82],"records":[85],"novel":[89],"circuitry":[90],"lab.":[93],"further":[95],"evaluated":[96],"compared":[98,142,182],"performance":[100],"with":[105,143,183,186,221],"existing":[107,226],"methods":[108,149,227],"on":[109,158],"two":[110],"independent":[111],"unseen":[113],"called":[115],"Chon":[116],"lab":[117],"artifact":[119],"dataset":[120,130,170],"II":[121,169],"(CMAD":[122],"II)":[123],"central":[125],"nervous":[126],"system":[127],"oxygen":[128],"toxicity":[129],"(CNS-OT).":[131],"RESULTS:":[132],")":[133],"lower":[135],"mean":[136,173],"squared":[137],"error":[138],"(":[139],"MSE)":[140],"when":[141,181],"three":[147],"previous":[148],"(averaged":[150],"[Formula:":[151],"text],":[153],"MSE":[155],"=":[156],"0.028":[157],"MA-corrupted":[160,198],"data).":[161],"Moreover,":[162],"reconstructed":[164],"EDAs":[165],"CMAD":[168],"had":[171,200],"correlation":[174,202],"value":[175,203],"0.78":[177],"(statistically":[178],"significantly":[179],"higher":[180,222],"other":[184],"methods)":[185],"reference":[188],"clean":[189],"motionless":[193],"hand,":[194],"whereas":[195],"raw":[197],"only":[205],"0.68.":[206],"CONCLUSION:":[207],"The":[208],"results":[209],"presented":[210],"paper":[213],"indicates":[214],"can":[218,233],"remove":[219],"MAs":[220],"intensity":[223],"where":[224],"fails.":[228],"SIGNIFICANCE:":[229],"Proposed":[230],"be":[234],"used":[235],"recover":[237],"significant":[239],"amount":[240],"otherwise":[242],"discarded":[243],"data.":[245]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":12},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":3}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}