{"id":"https://openalex.org/W4400188654","doi":"https://doi.org/10.1109/tce.2024.3421645","title":"An Electrocardiogram-Based Driver Stress Detection Scheme Using Ensembled Multiscale Classifier","display_name":"An Electrocardiogram-Based Driver Stress Detection Scheme Using Ensembled Multiscale Classifier","publication_year":2024,"publication_date":"2024-07-01","ids":{"openalex":"https://openalex.org/W4400188654","doi":"https://doi.org/10.1109/tce.2024.3421645"},"language":"en","primary_location":{"id":"doi:10.1109/tce.2024.3421645","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tce.2024.3421645","pdf_url":null,"source":{"id":"https://openalex.org/S126824455","display_name":"IEEE Transactions on Consumer Electronics","issn_l":"0098-3063","issn":["0098-3063","1558-4127"],"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 Consumer Electronics","raw_type":"journal-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/A5017156321","display_name":"Yang Wei","orcid":"https://orcid.org/0000-0003-3393-6211"},"institutions":[{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]}],"countries":["HK"],"is_corresponding":true,"raw_author_name":"Yang Wei","raw_affiliation_strings":["Department of Electrical Engineering, City University of Hong Kong, Hong Kong, SAR, China"],"raw_orcid":"https://orcid.org/0000-0003-3393-6211","affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, City University of Hong Kong, Hong Kong, SAR, China","institution_ids":["https://openalex.org/I168719708"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021393970","display_name":"Chung Kit Wu","orcid":"https://orcid.org/0000-0002-8230-6294"},"institutions":[{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Chung Kit Wu","raw_affiliation_strings":["Department of Electrical Engineering, City University of Hong Kong, Hong Kong, SAR, China"],"raw_orcid":"https://orcid.org/0000-0002-8230-6294","affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, City University of Hong Kong, Hong Kong, SAR, China","institution_ids":["https://openalex.org/I168719708"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5037894365","display_name":"Kim Fung Tsang","orcid":"https://orcid.org/0000-0002-8332-227X"},"institutions":[{"id":"https://openalex.org/I4210103346","display_name":"Nanfang Hospital","ror":"https://ror.org/01eq10738","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210103346"]},{"id":"https://openalex.org/I4400600959","display_name":"Nanfang College Guangzhou","ror":"https://ror.org/04vrxqg89","country_code":null,"type":"education","lineage":["https://openalex.org/I4400600959"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kim-Fung Tsang","raw_affiliation_strings":["School of Electrical and Computer Engineering, Nanfang College, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical and Computer Engineering, Nanfang College, Guangzhou, China","institution_ids":["https://openalex.org/I4210103346","https://openalex.org/I4400600959"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5017156321"],"corresponding_institution_ids":["https://openalex.org/I168719708"],"apc_list":null,"apc_paid":null,"fwci":1.5906,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.81787994,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"70","issue":"3","first_page":"5217","last_page":"5228"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11196","display_name":"Non-Invasive Vital Sign Monitoring","score":0.894599974155426,"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":0.894599974155426,"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":0.8827999830245972,"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":0.8615999817848206,"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/computer-science","display_name":"Computer science","score":0.556350588798523},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4776434004306793},{"id":"https://openalex.org/keywords/stress","display_name":"Stress (linguistics)","score":0.47157543897628784},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4240271747112274},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.4167846143245697},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4128655791282654}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.556350588798523},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4776434004306793},{"id":"https://openalex.org/C21036866","wikidata":"https://www.wikidata.org/wiki/Q181767","display_name":"Stress (linguistics)","level":2,"score":0.47157543897628784},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4240271747112274},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.4167846143245697},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4128655791282654},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tce.2024.3421645","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tce.2024.3421645","pdf_url":null,"source":{"id":"https://openalex.org/S126824455","display_name":"IEEE Transactions on Consumer Electronics","issn_l":"0098-3063","issn":["0098-3063","1558-4127"],"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 Consumer Electronics","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W1573113581","https://openalex.org/W1661614405","https://openalex.org/W1964029730","https://openalex.org/W1970207571","https://openalex.org/W1975183513","https://openalex.org/W1978431198","https://openalex.org/W2018613899","https://openalex.org/W2043756605","https://openalex.org/W2079155753","https://openalex.org/W2165058809","https://openalex.org/W2218733455","https://openalex.org/W2339837679","https://openalex.org/W2346299908","https://openalex.org/W2395515569","https://openalex.org/W2548996103","https://openalex.org/W2550780661","https://openalex.org/W2593216954","https://openalex.org/W2609112393","https://openalex.org/W2729660613","https://openalex.org/W2766792137","https://openalex.org/W2769912520","https://openalex.org/W2771140325","https://openalex.org/W2789522054","https://openalex.org/W2803205490","https://openalex.org/W2900361496","https://openalex.org/W2941820175","https://openalex.org/W2944163003","https://openalex.org/W2945280224","https://openalex.org/W2946751317","https://openalex.org/W2954469285","https://openalex.org/W2963005464","https://openalex.org/W2990808142","https://openalex.org/W2999516673","https://openalex.org/W3001866431","https://openalex.org/W3135644512","https://openalex.org/W3153065833","https://openalex.org/W3175829748","https://openalex.org/W3194188852","https://openalex.org/W3212549021","https://openalex.org/W4205599086","https://openalex.org/W4207030938","https://openalex.org/W4220818737","https://openalex.org/W4226180125","https://openalex.org/W4281659913","https://openalex.org/W4307014578","https://openalex.org/W4312610635","https://openalex.org/W6635264911"],"related_works":["https://openalex.org/W3188962172","https://openalex.org/W2772917594","https://openalex.org/W4306742369","https://openalex.org/W4303457083","https://openalex.org/W2131146434","https://openalex.org/W2951359407","https://openalex.org/W4376623224","https://openalex.org/W2033914206","https://openalex.org/W2042327336","https://openalex.org/W2129927767"],"abstract_inverted_index":{"Improving":[0],"driving":[1,49],"safety":[2],"is":[3,73,89],"the":[4,77,86,134,147,154,164,188],"key":[5],"to":[6,47,75,112],"reducing":[7],"traffic":[8,82],"accidents":[9],"caused":[10],"by":[11,133,153,171],"human":[12,28],"factors":[13,29],"such":[14],"as":[15],"cognitive":[16],"errors,":[17,19],"judgment":[18],"slow":[20],"emergency":[21],"response,":[22],"etc.":[23],"Studies":[24],"have":[25,44],"shown":[26],"these":[27],"are":[30,110,125,144,169],"highly":[31],"correlated":[32],"with":[33,137],"drivers\u2019":[34,182],"stress":[35,40,87,183],"levels.":[36,184],"Hence,":[37],"various":[38],"driver":[39],"detection":[41,60,117,197],"(DSD)":[42],"schemes":[43],"been":[45],"developed":[46,189],"improve":[48],"safety.":[50],"However,":[51],"existing":[52],"approaches":[53],"face":[54],"great":[55],"difficulties":[56],"in":[57],"achieving":[58],"high":[59,192],"accuracy":[61,118],"and":[62,79,101,107,119,131,151,166,195],"practicability.":[63,121],"To":[64],"address":[65],"this":[66],"challenge,":[67],"an":[68,172],"Ensembled":[69],"Multiscale":[70],"Classifier":[71],"(EMC)":[72],"proposed":[74],"realize":[76],"DSD":[78],"further":[80],"reduce":[81],"accidents.":[83],"In":[84],"EMC,":[85],"level":[88],"classified":[90],"into":[91],"three":[92],"categories,":[93],"namely":[94],"Low-Stress":[95],"Level":[96,99,103],"(LSL),":[97],"Mid-Stress":[98],"(MSL),":[100],"High-Stress":[102],"(HSL).":[104],"Fiducial":[105],"features":[106,109,124],"non-fiducial":[108,141],"considered":[111],"reach":[113],"a":[114,178],"balance":[115],"between":[116],"implementation":[120],"Specifically,":[122],"fiducial":[123],"extracted":[126,145],"directly":[127],"from":[128,146],"ECG":[129,149],"signals":[130,150],"analyzed":[132,152],"neural":[135,157],"network":[136,158],"backpropagation":[138],"(NNBP).":[139],"For":[140],"features,":[142],"they":[143],"transformed":[148],"1-D":[155,167],"convolutional":[156],"(1-D":[159],"CNN).":[160],"The":[161],"outputs":[162],"of":[163,181],"NNBP":[165],"CNN":[168],"coordinated":[170],"ensembled":[173],"decision-making":[174],"layer,":[175],"thereby":[176],"deriving":[177],"probabilistic":[179],"prediction":[180],"Experimental":[185],"results":[186],"reveal":[187],"method":[190],"has":[191],"model":[193],"fitness":[194],"95.9%":[196],"accuracy.":[198]},"counts_by_year":[{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":1}],"updated_date":"2025-12-23T23:11:35.936235","created_date":"2025-10-10T00:00:00"}
