{"id":"https://openalex.org/W2995839309","doi":"https://doi.org/10.1109/tencon.2019.8929254","title":"Performance analysis of deep learning CNN in classification of depression EEG signals","display_name":"Performance analysis of deep learning CNN in classification of depression EEG signals","publication_year":2019,"publication_date":"2019-10-01","ids":{"openalex":"https://openalex.org/W2995839309","doi":"https://doi.org/10.1109/tencon.2019.8929254","mag":"2995839309"},"language":"en","primary_location":{"id":"doi:10.1109/tencon.2019.8929254","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tencon.2019.8929254","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON)","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/A5042955638","display_name":"P Sandheep","orcid":null},"institutions":[{"id":"https://openalex.org/I114845381","display_name":"National Institute of Technology Calicut","ror":"https://ror.org/03yyd7552","country_code":"IN","type":"education","lineage":["https://openalex.org/I114845381"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"P Sandheep","raw_affiliation_strings":["Department of Electrical Engineering, NIT, Calicut, India"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, NIT, Calicut, India","institution_ids":["https://openalex.org/I114845381"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086335765","display_name":"S Vineeth","orcid":"https://orcid.org/0000-0002-6605-4795"},"institutions":[{"id":"https://openalex.org/I114845381","display_name":"National Institute of Technology Calicut","ror":"https://ror.org/03yyd7552","country_code":"IN","type":"education","lineage":["https://openalex.org/I114845381"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"S Vineeth","raw_affiliation_strings":["Department of Electrical Engineering, NIT, Calicut, India"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, NIT, Calicut, India","institution_ids":["https://openalex.org/I114845381"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071090722","display_name":"Meljo Poulose","orcid":null},"institutions":[{"id":"https://openalex.org/I114845381","display_name":"National Institute of Technology Calicut","ror":"https://ror.org/03yyd7552","country_code":"IN","type":"education","lineage":["https://openalex.org/I114845381"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Meljo Poulose","raw_affiliation_strings":["Department of Electrical Engineering, NIT, Calicut, India"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, NIT, Calicut, India","institution_ids":["https://openalex.org/I114845381"]}]},{"author_position":"last","author":{"id":null,"display_name":"D P Subha","orcid":null},"institutions":[{"id":"https://openalex.org/I114845381","display_name":"National Institute of Technology Calicut","ror":"https://ror.org/03yyd7552","country_code":"IN","type":"education","lineage":["https://openalex.org/I114845381"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"D P Subha","raw_affiliation_strings":["Department of Electrical Engineering, NIT, Calicut, India"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, NIT, Calicut, India","institution_ids":["https://openalex.org/I114845381"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5042955638"],"corresponding_institution_ids":["https://openalex.org/I114845381"],"apc_list":null,"apc_paid":null,"fwci":2.2328,"has_fulltext":false,"cited_by_count":39,"citation_normalized_percentile":{"value":0.88028645,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1339","last_page":"1344"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":1.0,"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":1.0,"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/T11021","display_name":"ECG Monitoring and Analysis","score":0.9962000250816345,"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/T10241","display_name":"Functional Brain Connectivity Studies","score":0.9861000180244446,"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/electroencephalography","display_name":"Electroencephalography","score":0.8038469552993774},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7384272813796997},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7269836068153381},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6918070912361145},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6856354475021362},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6265074014663696},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5549834966659546},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.44890302419662476},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37270987033843994},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.335446298122406},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.1695372760295868},{"id":"https://openalex.org/keywords/neuroscience","display_name":"Neuroscience","score":0.0730801522731781}],"concepts":[{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.8038469552993774},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7384272813796997},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7269836068153381},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6918070912361145},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6856354475021362},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6265074014663696},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5549834966659546},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.44890302419662476},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37270987033843994},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.335446298122406},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.1695372760295868},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.0730801522731781}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tencon.2019.8929254","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tencon.2019.8929254","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W38454739","https://openalex.org/W801922083","https://openalex.org/W1964357132","https://openalex.org/W1967217003","https://openalex.org/W1969500052","https://openalex.org/W1975717934","https://openalex.org/W1995706182","https://openalex.org/W2080146971","https://openalex.org/W2081700482","https://openalex.org/W2090704006","https://openalex.org/W2102244548","https://openalex.org/W2129094939","https://openalex.org/W2140434576","https://openalex.org/W2158076175","https://openalex.org/W2416749705","https://openalex.org/W2551054676","https://openalex.org/W2605518258","https://openalex.org/W2739093060","https://openalex.org/W2759483166","https://openalex.org/W2800428573","https://openalex.org/W2889245000","https://openalex.org/W2964065019","https://openalex.org/W4241710964","https://openalex.org/W6622888473","https://openalex.org/W6641939399","https://openalex.org/W6716372715"],"related_works":["https://openalex.org/W2922348724","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3167935049","https://openalex.org/W3103566983","https://openalex.org/W3029198973","https://openalex.org/W2032664813"],"abstract_inverted_index":{"With":[0],"the":[1,25,29,76,92,96,101,105,113,120,128,181],"advent":[2],"of":[3,24,31,79,87,95,104,112,130,136,155,161],"greater":[4],"computing":[5],"power":[6],"each":[7],"year,":[8],"computer-based":[9],"disease/condition":[10],"diagnosis":[11],"have":[12],"been":[13],"gaining":[14],"significant":[15],"importance":[16],"recently.":[17],"In":[18],"this":[19,56],"paper,":[20],"an":[21],"extensive":[22,142],"analysis":[23],"approach":[26,145],"based":[27],"on":[28],"classification":[30,121],"depression":[32,83,150],"using":[33,63,162],"electroencephalogram":[34],"(EEG)":[35],"signals":[36,65,86,152],"is":[37,53,146,165],"carried":[38],"out.":[39],"A":[40],"computer-aided":[41],"machine":[42],"learning":[43,51,132,144,164],"approach:":[44],"Convolutional":[45],"Neural":[46],"Network":[47],"(CNN),":[48],"a":[49],"deep":[50,59,163],"method":[52],"used":[54],"in":[55,81],"work.":[57],"The":[58,73,110,158],"CNN":[60,114],"was":[61,116],"trained":[62],"EEG":[64,85,151],"from":[66,84,91,100,153,180],"30":[67,70],"normal":[68,88],"and":[69,98,138,171],"depressed":[71],"persons.":[72],"network":[74,115],"attained":[75],"highest":[77],"accuracy":[78,170],"99.31%":[80],"classifying":[82],"controls":[89],"recorded":[90],"right":[93],"hemisphere":[94,103],"brain":[97,106],"96.3%":[99],"left":[102],"after":[107],"ten-fold":[108],"cross-validation.":[109],"performance":[111],"evaluated":[117],"by":[118],"evaluating":[119],"accuracy,":[122],"varying":[123],"different":[124],"parameters":[125],"such":[126],"as":[127],"number":[129,135],"strides,":[131],"rate":[133],"parameter,":[134],"epochs,":[137],"sample":[139],"size.":[140],"An":[141],"data":[143],"proposed":[147],"to":[148],"classify":[149],"that":[154,166],"healthy":[156],"controls.":[157],"key":[159],"advantage":[160],"they":[167],"return":[168],"state-of-the-art":[169],"do":[172],"not":[173],"require":[174],"manual":[175],"pre-processing":[176],"or":[177],"feature":[178],"extraction":[179],"signal.":[182]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":10},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":11},{"year":2020,"cited_by_count":2}],"updated_date":"2026-04-16T08:26:57.006410","created_date":"2025-10-10T00:00:00"}
