{"id":"https://openalex.org/W4294643431","doi":"https://doi.org/10.1109/tim.2022.3204076","title":"VHERS: A Novel Variational Mode Decomposition and Hilbert Transform-Based EEG Rhythm Separation for Automatic ADHD Detection","display_name":"VHERS: A Novel Variational Mode Decomposition and Hilbert Transform-Based EEG Rhythm Separation for Automatic ADHD Detection","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4294643431","doi":"https://doi.org/10.1109/tim.2022.3204076"},"language":"en","primary_location":{"id":"doi:10.1109/tim.2022.3204076","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2022.3204076","pdf_url":null,"source":{"id":"https://openalex.org/S10892749","display_name":"IEEE Transactions on Instrumentation and Measurement","issn_l":"0018-9456","issn":["0018-9456","1557-9662"],"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 Instrumentation and Measurement","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/A5008824968","display_name":"Smith K. Khare","orcid":"https://orcid.org/0000-0001-8365-1092"},"institutions":[{"id":"https://openalex.org/I204337017","display_name":"Aarhus University","ror":"https://ror.org/01aj84f44","country_code":"DK","type":"education","lineage":["https://openalex.org/I204337017"]}],"countries":["DK"],"is_corresponding":true,"raw_author_name":"Smith K. Khare","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Aarhus University, Aarhus N, Denmark"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Aarhus University, Aarhus N, Denmark","institution_ids":["https://openalex.org/I204337017"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072708986","display_name":"Nikhil B. Gaikwad","orcid":"https://orcid.org/0000-0002-6119-2099"},"institutions":[{"id":"https://openalex.org/I204337017","display_name":"Aarhus University","ror":"https://ror.org/01aj84f44","country_code":"DK","type":"education","lineage":["https://openalex.org/I204337017"]}],"countries":["DK"],"is_corresponding":false,"raw_author_name":"Nikhil B. Gaikwad","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Aarhus University, Aarhus N, Denmark"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Aarhus University, Aarhus N, Denmark","institution_ids":["https://openalex.org/I204337017"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5059215546","display_name":"Varun Bajaj","orcid":"https://orcid.org/0000-0002-8721-1219"},"institutions":[{"id":"https://openalex.org/I207223250","display_name":"Indian Institute of Information Technology Design and Manufacturing Jabalpur","ror":"https://ror.org/00gmd7q80","country_code":"IN","type":"education","lineage":["https://openalex.org/I207223250"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Varun Bajaj","raw_affiliation_strings":["Electronics and Communication Discipline, Indian Institute of Information Technology, Design and Manufacturing (IIITDM) Jabalpur, Jabalpur, India"],"affiliations":[{"raw_affiliation_string":"Electronics and Communication Discipline, Indian Institute of Information Technology, Design and Manufacturing (IIITDM) Jabalpur, Jabalpur, India","institution_ids":["https://openalex.org/I207223250"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5008824968"],"corresponding_institution_ids":["https://openalex.org/I204337017"],"apc_list":null,"apc_paid":null,"fwci":5.5312,"has_fulltext":false,"cited_by_count":50,"citation_normalized_percentile":{"value":0.96953655,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":"71","issue":null,"first_page":"1","last_page":"10"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9998000264167786,"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":0.9998000264167786,"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/T10745","display_name":"Heart Rate Variability and Autonomic Control","score":0.9884999990463257,"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/T11021","display_name":"ECG Monitoring and Analysis","score":0.9732000231742859,"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/hilbert\u2013huang-transform","display_name":"Hilbert\u2013Huang transform","score":0.7151924967765808},{"id":"https://openalex.org/keywords/electroencephalography","display_name":"Electroencephalography","score":0.6254830360412598},{"id":"https://openalex.org/keywords/decomposition","display_name":"Decomposition","score":0.6096574068069458},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.5433219075202942},{"id":"https://openalex.org/keywords/hilbert-transform","display_name":"Hilbert transform","score":0.5315808057785034},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4916013181209564},{"id":"https://openalex.org/keywords/rhythm","display_name":"Rhythm","score":0.4805717468261719},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.46765440702438354},{"id":"https://openalex.org/keywords/mode","display_name":"Mode (computer interface)","score":0.4572983682155609},{"id":"https://openalex.org/keywords/separation","display_name":"Separation (statistics)","score":0.44356536865234375},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4410000443458557},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.40239256620407104},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.357657790184021},{"id":"https://openalex.org/keywords/acoustics","display_name":"Acoustics","score":0.21533575654029846},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.19161775708198547},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.14515191316604614},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.12625154852867126},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.11141911149024963},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.08975797891616821},{"id":"https://openalex.org/keywords/white-noise","display_name":"White noise","score":0.08170697093009949},{"id":"https://openalex.org/keywords/spectral-density","display_name":"Spectral density","score":0.0688515305519104}],"concepts":[{"id":"https://openalex.org/C25570617","wikidata":"https://www.wikidata.org/wiki/Q1006462","display_name":"Hilbert\u2013Huang transform","level":3,"score":0.7151924967765808},{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.6254830360412598},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.6096574068069458},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.5433219075202942},{"id":"https://openalex.org/C28799612","wikidata":"https://www.wikidata.org/wiki/Q685437","display_name":"Hilbert transform","level":3,"score":0.5315808057785034},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4916013181209564},{"id":"https://openalex.org/C135343436","wikidata":"https://www.wikidata.org/wiki/Q170406","display_name":"Rhythm","level":2,"score":0.4805717468261719},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46765440702438354},{"id":"https://openalex.org/C48677424","wikidata":"https://www.wikidata.org/wiki/Q6888088","display_name":"Mode (computer interface)","level":2,"score":0.4572983682155609},{"id":"https://openalex.org/C2776061190","wikidata":"https://www.wikidata.org/wiki/Q7451805","display_name":"Separation (statistics)","level":2,"score":0.44356536865234375},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4410000443458557},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40239256620407104},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.357657790184021},{"id":"https://openalex.org/C24890656","wikidata":"https://www.wikidata.org/wiki/Q82811","display_name":"Acoustics","level":1,"score":0.21533575654029846},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.19161775708198547},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.14515191316604614},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.12625154852867126},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.11141911149024963},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.08975797891616821},{"id":"https://openalex.org/C112633086","wikidata":"https://www.wikidata.org/wiki/Q381287","display_name":"White noise","level":2,"score":0.08170697093009949},{"id":"https://openalex.org/C168110828","wikidata":"https://www.wikidata.org/wiki/Q1331626","display_name":"Spectral density","level":2,"score":0.0688515305519104},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C118552586","wikidata":"https://www.wikidata.org/wiki/Q7867","display_name":"Psychiatry","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tim.2022.3204076","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2022.3204076","pdf_url":null,"source":{"id":"https://openalex.org/S10892749","display_name":"IEEE Transactions on Instrumentation and Measurement","issn_l":"0018-9456","issn":["0018-9456","1557-9662"],"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 Instrumentation and Measurement","raw_type":"journal-article"},{"id":"pmh:oai:pure.atira.dk:publications/19494ee7-0c42-4e60-b770-fc6fe09a86cd","is_oa":false,"landing_page_url":"https://pure.au.dk/portal/en/publications/19494ee7-0c42-4e60-b770-fc6fe09a86cd","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Khare, S K, Gaikwad, N & Bajaj, V 2022, 'VHERS : A Novel Variational Mode Decomposition and Hilbert Transform-Based EEG Rhythm Separation for Automatic ADHD Detection', IEEE Transactions on Instrumentation and Measurement, vol. 71, 4008310, pp. 1-10. https://doi.org/10.1109/TIM.2022.3204076","raw_type":"info:eu-repo/semantics/publishedVersion"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","score":0.5099999904632568,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":51,"referenced_works":["https://openalex.org/W1973287125","https://openalex.org/W1983030849","https://openalex.org/W1997843451","https://openalex.org/W2000982976","https://openalex.org/W2007221293","https://openalex.org/W2027927824","https://openalex.org/W2035525391","https://openalex.org/W2042891941","https://openalex.org/W2045869251","https://openalex.org/W2051005710","https://openalex.org/W2051485226","https://openalex.org/W2071756182","https://openalex.org/W2073259742","https://openalex.org/W2089727665","https://openalex.org/W2102388069","https://openalex.org/W2111072639","https://openalex.org/W2115308275","https://openalex.org/W2119630093","https://openalex.org/W2122953786","https://openalex.org/W2128947758","https://openalex.org/W2134429331","https://openalex.org/W2142408637","https://openalex.org/W2420049378","https://openalex.org/W2562793216","https://openalex.org/W2577467065","https://openalex.org/W2725456721","https://openalex.org/W2755986543","https://openalex.org/W2773657926","https://openalex.org/W2939282900","https://openalex.org/W2955436135","https://openalex.org/W2955848854","https://openalex.org/W2963261317","https://openalex.org/W2981519511","https://openalex.org/W3006260767","https://openalex.org/W3016163744","https://openalex.org/W3040413318","https://openalex.org/W3080276055","https://openalex.org/W3081115829","https://openalex.org/W3083067908","https://openalex.org/W3102403537","https://openalex.org/W3119714845","https://openalex.org/W3145713205","https://openalex.org/W3154664959","https://openalex.org/W3163147779","https://openalex.org/W3176566184","https://openalex.org/W3196283869","https://openalex.org/W3203469066","https://openalex.org/W4210485604","https://openalex.org/W4214688960","https://openalex.org/W6676577790","https://openalex.org/W6746642624"],"related_works":["https://openalex.org/W2083592477","https://openalex.org/W2363056446","https://openalex.org/W2353960620","https://openalex.org/W3190676168","https://openalex.org/W2107880197","https://openalex.org/W2074184731","https://openalex.org/W1986719249","https://openalex.org/W2004948286","https://openalex.org/W154554909","https://openalex.org/W2386521116"],"abstract_inverted_index":{"Background:":[0],"Attention":[1],"deficit":[2],"hyperactivity":[3],"disorder":[4],"(ADHD)":[5],"is":[6,77,209],"an":[7],"isogenous":[8],"pattern":[9],"of":[10,36,53,63,142,149,153,157,161,166,173,180,215],"hyperactivity,":[11],"impulsivity,":[12],"and":[13,23,41,70,84,94,102,111,126,128,168,199,218],"inattention,":[14],"resulting":[15],"in":[16,21,232],"disorders":[17],"like":[18],"anxiety,":[19],"disability":[20],"learning,":[22],"depression.":[24],"The":[25,79,97,134,186,203,222],"electroencephalogram":[26],"(EEG)":[27],"signals":[28],"are":[29,88,105,116],"a":[30,56,66,147,177],"valuable":[31],"source":[32],"for":[33,59,206],"early":[34],"detection":[35,62],"ADHD.":[37,64],"However,":[38],"EEG\u2019s":[39],"non-linear":[40],"non-stationary":[42],"nature":[43,214],"makes":[44],"its":[45],"direct":[46],"analysis":[47,124],"very":[48],"difficult.":[49],"Method:":[50],"Different":[51,113],"rhythms":[52,104],"EEG":[54,73,201,220],"offer":[55],"robust":[57],"solution":[58],"the":[60,108,119,139,155,171,190,212,216],"automatic":[61],"Therefore,":[65],"novel":[67],"variational":[68,91],"mode":[69,92],"Hilbert":[71,95],"transform-based":[72],"rhythm":[74,192,208],"separation":[75],"(VHERS)":[76],"developed.":[78],"instantaneous":[80,85],"frequency":[81],"envelops":[82],"(IFE),":[83],"amplitude":[86],"(IA)":[87],"extracted":[89],"using":[90,122,130,176],"decomposition":[93],"transform.":[96],"delta,":[98],"theta,":[99],"alpha,":[100],"beta,":[101],"gamma":[103,207],"constructed":[106],"from":[107,118],"corresponding":[109],"IFE":[110],"IA.":[112],"entropy-based":[114],"features":[115],"evaluated":[117],"rhythms,":[120],"selected":[121],"statistical":[123],"(mean":[125],"STD),":[127],"classified":[129],"multiple":[131],"techniques.":[132],"Results:":[133],"proposed":[135,223],"VHERS":[136,224],"has":[137,193],"obtained":[138],"highest":[140],"performance":[141,187,205],"100%":[143],"sensitivity,":[144],"99.95%":[145],"accuracy,":[146],"specificity":[148],"99.89%,":[150],"Cohen\u2019s":[151],"Kappa":[152],"99.9%,":[154,167],"precision":[156],"99.91%,":[158],"F-1":[159],"score":[160],"0.999,":[162],"Mathews":[163],"correlation":[164],"coefficient":[165],"area":[169],"under":[170],"curve":[172],"99.95%,":[174],"respectively":[175],"sigmoid":[178],"kernel":[179],"extreme":[181],"learning":[182],"machine":[183],"classifier.":[184],"Conclusion:":[185],"shows":[188],"that":[189],"delta":[191],"provided":[194],"more":[195],"insight":[196],"into":[197],"ADHD":[198,217,231],"NC":[200,219],"signals.":[202],"degraded":[204],"due":[210],"to":[211,229],"overlapping":[213],"features.":[221],"model":[225],"can":[226],"help":[227],"experts":[228],"detect":[230],"real-time":[233],"scenarios.":[234]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":14},{"year":2024,"cited_by_count":15},{"year":2023,"cited_by_count":17},{"year":2022,"cited_by_count":1}],"updated_date":"2026-03-09T08:58:05.943551","created_date":"2025-10-10T00:00:00"}
