{"id":"https://openalex.org/W7151279407","doi":"https://doi.org/10.1109/icmla66185.2025.00081","title":"EDLAD: Ensemble Deep Learning for Alzheimer\u2019s Disease Detection using Brain EEG Signals","display_name":"EDLAD: Ensemble Deep Learning for Alzheimer\u2019s Disease Detection using Brain EEG Signals","publication_year":2025,"publication_date":"2025-12-03","ids":{"openalex":"https://openalex.org/W7151279407","doi":"https://doi.org/10.1109/icmla66185.2025.00081"},"language":null,"primary_location":{"id":"doi:10.1109/icmla66185.2025.00081","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmla66185.2025.00081","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Machine Learning and Applications (ICMLA)","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/A5133072849","display_name":"An T.T. Phan","orcid":null},"institutions":[{"id":"https://openalex.org/I63772739","display_name":"Florida Atlantic University","ror":"https://ror.org/05p8w6387","country_code":"US","type":"education","lineage":["https://openalex.org/I63772739"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"An T.T. Phan","raw_affiliation_strings":["Florida Atlantic University,Dept. of Electrical Engineering and Computer Science,Boca Raton,FL,USA"],"affiliations":[{"raw_affiliation_string":"Florida Atlantic University,Dept. of Electrical Engineering and Computer Science,Boca Raton,FL,USA","institution_ids":["https://openalex.org/I63772739"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5109916319","display_name":"Xingquan Zhu","orcid":null},"institutions":[{"id":"https://openalex.org/I63772739","display_name":"Florida Atlantic University","ror":"https://ror.org/05p8w6387","country_code":"US","type":"education","lineage":["https://openalex.org/I63772739"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xingquan Zhu","raw_affiliation_strings":["Florida Atlantic University,Dept. of Electrical Engineering and Computer Science,Boca Raton,FL,USA"],"affiliations":[{"raw_affiliation_string":"Florida Atlantic University,Dept. of Electrical Engineering and Computer Science,Boca Raton,FL,USA","institution_ids":["https://openalex.org/I63772739"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5133072849"],"corresponding_institution_ids":["https://openalex.org/I63772739"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.7532441,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"555","last_page":"562"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.8528000116348267,"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":0.8528000116348267,"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/T12702","display_name":"Brain Tumor Detection and Classification","score":0.016699999570846558,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"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/T13702","display_name":"Machine Learning in Healthcare","score":0.0142000000923872,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6245999932289124},{"id":"https://openalex.org/keywords/electroencephalography","display_name":"Electroencephalography","score":0.4731999933719635},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4462999999523163},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.4052000045776367},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.34459999203681946},{"id":"https://openalex.org/keywords/disease","display_name":"Disease","score":0.31690001487731934}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6852999925613403},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6245999932289124},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5853999853134155},{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.4731999933719635},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4462999999523163},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.4052000045776367},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.34459999203681946},{"id":"https://openalex.org/C2779134260","wikidata":"https://www.wikidata.org/wiki/Q12136","display_name":"Disease","level":2,"score":0.31690001487731934},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3165000081062317},{"id":"https://openalex.org/C2778542668","wikidata":"https://www.wikidata.org/wiki/Q618076","display_name":"Deep brain stimulation","level":4,"score":0.3151000142097473},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3095000088214874},{"id":"https://openalex.org/C2991673738","wikidata":"https://www.wikidata.org/wiki/Q5062122","display_name":"Brain disease","level":3,"score":0.2838999927043915},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2596000134944916}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icmla66185.2025.00081","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmla66185.2025.00081","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Machine Learning and Applications (ICMLA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W2134050473","https://openalex.org/W2805246634","https://openalex.org/W2838070964","https://openalex.org/W2996150132","https://openalex.org/W3013699712","https://openalex.org/W3115292829","https://openalex.org/W3122299217","https://openalex.org/W4225319948","https://openalex.org/W4280556554","https://openalex.org/W4281972751","https://openalex.org/W4291910446","https://openalex.org/W4292338353","https://openalex.org/W4312597583","https://openalex.org/W4318677156","https://openalex.org/W4377157636","https://openalex.org/W4384080511","https://openalex.org/W4396560090","https://openalex.org/W4396624676","https://openalex.org/W4400679620","https://openalex.org/W4400771295","https://openalex.org/W4401813412","https://openalex.org/W4403868738","https://openalex.org/W4408168261"],"related_works":[],"abstract_inverted_index":{"Due":[0],"to":[1,88,129,153],"its":[2],"low":[3],"cost":[4],"and":[5,19,46,50,110,124,141,146,158,187,201],"non-invasive":[6],"nature,":[7],"electroencephalography":[8],"(EEG)":[9],"is":[10,127],"widely":[11],"used":[12],"in":[13,20,39,78,138],"the":[14,26,94,122,131,139,155,160,163,174,181,185,197,218],"diagnosis":[15],"of":[16,28,81,96,162],"neurological":[17],"disorders":[18],"monitoring":[21],"cognitive":[22],"states,":[23],"such":[24],"as":[25],"detection":[27,41,69],"Alzheimer\u2019s":[29,68],"disease":[30],"(AD).":[31],"While":[32],"deep":[33,64,73,101],"learning":[34,65,74,87,102,156],"methods":[35,211],"have":[36],"shown":[37],"promise":[38],"AD":[40],"from":[42,217],"EEG,":[43],"signal":[44],"noise":[45],"variability":[47],"across":[48,190],"channels":[49],"recording":[51],"durations":[52],"remain":[53],"significant":[54],"challenges.":[55],"In":[56,135],"this":[57],"study,":[58],"we":[59],"propose":[60],"EDLAD,":[61,178],"an":[62],"ensemble":[63,86,176,204],"approach":[66],"for":[67],"that":[70,173,212],"integrates":[71,121],"multiple":[72,97],"models,":[75],"each":[76],"specialized":[77],"different":[79],"types":[80],"EEG-derived":[82],"features.":[83],"EDLAD":[84,207],"leverages":[85],"improve":[89,159],"predictive":[90],"performance":[91],"by":[92],"combining":[93],"outputs":[95],"models.":[98,220],"Specifically,":[99],"three":[100,219],"models":[103],"are":[104,144],"independently":[105],"trained:":[106],"a":[107,202],"Transformer-based":[108,116],"model":[109,200],"two":[111,149,169],"dense":[112],"network":[113],"classifiers.":[114],"The":[115],"model,":[117],"called":[118],"Conformer,":[119],"which":[120],"convolutional":[123],"transformer":[125],"layers,":[126],"designed":[128],"process":[130,157],"original":[132],"EEG":[133,170],"signals.":[134],"parallel,":[136],"features":[137],"time":[140],"frequency":[142],"domains":[143],"extracted":[145],"fed":[147],"into":[148],"separate":[150],"neural":[151],"networks":[152],"complement":[154],"robustness":[161],"final":[164],"predictions.":[165],"Experiments":[166],"conducted":[167],"on":[168],"datasets":[171],"demonstrate":[172],"proposed":[175],"framework,":[177],"consistently":[179],"improves":[180],"classification":[182],"accuracy":[183],"at":[184],"segment":[186],"subject":[188],"levels":[189],"all":[191],"window":[192],"lengths.":[193],"It":[194],"outperforms":[195],"both":[196],"single":[198],"Conformer":[199],"meta-learning":[203],"approach.":[205],"Furthermore,":[206],"surpasses":[208],"feature-level":[209],"fusion":[210],"simply":[213],"concatenate":[214],"feature":[215],"representations":[216]},"counts_by_year":[],"updated_date":"2026-04-09T06:08:40.794217","created_date":"2026-04-08T00:00:00"}
