{"id":"https://openalex.org/W7216779581","doi":"https://doi.org/10.5281/zenodo.24146031","title":"Systematic Evaluation of Deep Learning Architectures for Epilepsy Detection with Patient-Based Validation","display_name":"Systematic Evaluation of Deep Learning Architectures for Epilepsy Detection with Patient-Based Validation","publication_year":2026,"publication_date":"2026-11-11","ids":{"openalex":"https://openalex.org/W7216779581","doi":"https://doi.org/10.5281/zenodo.24146031"},"language":"en","primary_location":{"id":"pmh:oai:oai.as-proceeding.com:article/2879","doi":null,"is_oa":true,"landing_page_url":"https://as-proceeding.com/index.php/ijanser/article/view/2879","pdf_url":"https://as-proceeding.com/index.php/ijanser/article/download/2879/2735","source":{"id":"https://openalex.org/S4387288395","display_name":"International Journal of Advanced Natural Sciences and Engineering Researches","issn_l":"2980-0811","issn":["2980-0811"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2980-0811","raw_type":"info:eu-repo/semantics/article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://as-proceeding.com/index.php/ijanser/article/download/2879/2735","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5161480985","display_name":"All Sciences Academy","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"All Sciences Academy","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5161480985"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9984999895095825,"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":0.9984999895095825,"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":0.00039999998989515007,"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/T10667","display_name":"Emotion and Mood Recognition","score":0.00019999999494757503,"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/seizure-detection","display_name":"seizure detection","score":0.9430000185966492},{"id":"https://openalex.org/keywords/eeg-signal-processing","display_name":"EEG signal processing","score":0.7590000033378601},{"id":"https://openalex.org/keywords/1d-convolutional-neural-network","display_name":"1D convolutional neural network","score":0.8240000009536743},{"id":"https://openalex.org/keywords/bilstm","display_name":"BiLSTM","score":0.8870000243186951},{"id":"https://openalex.org/keywords/attention-mechanism","display_name":"attention mechanism","score":0.9660000205039978},{"id":"https://openalex.org/keywords/time-frequency-representation","display_name":"time-frequency representation","score":0.7160000205039978},{"id":"https://openalex.org/keywords/time-domain-representation","display_name":"time-domain representation","score":0.8420000076293945},{"id":"https://openalex.org/keywords/model-generalization","display_name":"model generalization","score":0.7099999785423279},{"id":"https://openalex.org/keywords/inference-speed","display_name":"inference speed","score":0.48100000619888306}],"concepts":[{"id":"https://openalex.org/C2778186239","wikidata":"https://www.wikidata.org/wiki/Q41571","display_name":"Epilepsy","level":2,"score":0.7398999929428101},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7239000201225281},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6800000071525574},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6699000000953674},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6589999794960022},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.623199999332428},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5217999815940857},{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.4140999913215637},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.382999986410141},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.3815999925136566},{"id":"https://openalex.org/C5465570","wikidata":"https://www.wikidata.org/wiki/Q5326898","display_name":"Early stopping","level":3,"score":0.37709999084472656},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.3691999912261963},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.3589000105857849},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3476000130176544},{"id":"https://openalex.org/C2779334592","wikidata":"https://www.wikidata.org/wiki/Q6279182","display_name":"Epileptic seizure","level":3,"score":0.30889999866485596},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.30320000648498535},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.28870001435279846},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.28790000081062317},{"id":"https://openalex.org/C2985946229","wikidata":"https://www.wikidata.org/wiki/Q49908","display_name":"Data sampling","level":2,"score":0.28060001134872437},{"id":"https://openalex.org/C80478641","wikidata":"https://www.wikidata.org/wiki/Q195771","display_name":"Sequential analysis","level":2,"score":0.2800999879837036},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2782000005245209},{"id":"https://openalex.org/C168031717","wikidata":"https://www.wikidata.org/wiki/Q1530280","display_name":"Balance (ability)","level":2,"score":0.2734000086784363},{"id":"https://openalex.org/C2777267654","wikidata":"https://www.wikidata.org/wiki/Q3519023","display_name":"Test (biology)","level":2,"score":0.27320000529289246},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2687000036239624},{"id":"https://openalex.org/C2984391234","wikidata":"https://www.wikidata.org/wiki/Q195771","display_name":"Sequential sampling","level":3,"score":0.26429998874664307}],"mesh":[],"locations_count":3,"locations":[{"id":"pmh:oai:oai.as-proceeding.com:article/2879","doi":null,"is_oa":true,"landing_page_url":"https://as-proceeding.com/index.php/ijanser/article/view/2879","pdf_url":"https://as-proceeding.com/index.php/ijanser/article/download/2879/2735","source":{"id":"https://openalex.org/S4387288395","display_name":"International Journal of Advanced Natural Sciences and Engineering Researches","issn_l":"2980-0811","issn":["2980-0811"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2980-0811","raw_type":"info:eu-repo/semantics/article"},{"id":"doi:10.5281/zenodo.24146031","doi":"https://doi.org/10.5281/zenodo.24146031","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.24146031","pdf_url":null,"source":{"id":"https://openalex.org/S4387288395","display_name":"International Journal of Advanced Natural Sciences and Engineering Researches","issn_l":"2980-0811","issn":["2980-0811"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Book"},{"id":"doi:10.5281/zenodo.24146032","doi":"https://doi.org/10.5281/zenodo.24146032","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.24146032","pdf_url":null,"source":{"id":"https://openalex.org/S4387288395","display_name":"International Journal of Advanced Natural Sciences and Engineering Researches","issn_l":"2980-0811","issn":["2980-0811"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Book"}],"best_oa_location":{"id":"pmh:oai:oai.as-proceeding.com:article/2879","doi":null,"is_oa":true,"landing_page_url":"https://as-proceeding.com/index.php/ijanser/article/view/2879","pdf_url":"https://as-proceeding.com/index.php/ijanser/article/download/2879/2735","source":{"id":"https://openalex.org/S4387288395","display_name":"International Journal of Advanced Natural Sciences and Engineering Researches","issn_l":"2980-0811","issn":["2980-0811"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2980-0811","raw_type":"info:eu-repo/semantics/article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","score":0.8048999905586243,"display_name":"Good health and well-being"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7216779581.pdf","grobid_xml":"https://content.openalex.org/works/W7216779581.grobid-xml"},"referenced_works_count":17,"referenced_works":["https://openalex.org/W2046596317","https://openalex.org/W2162800060","https://openalex.org/W3020710563","https://openalex.org/W3037360817","https://openalex.org/W3123356289","https://openalex.org/W3130743277","https://openalex.org/W4205310537","https://openalex.org/W4206576602","https://openalex.org/W4281552331","https://openalex.org/W4281724608","https://openalex.org/W4286247627","https://openalex.org/W4385225650","https://openalex.org/W4399513346","https://openalex.org/W4404910754","https://openalex.org/W4410094071","https://openalex.org/W4410463668","https://openalex.org/W6940928799"],"related_works":["https://openalex.org/W4416939178","https://openalex.org/W4391878030","https://openalex.org/W4416375303","https://openalex.org/W4324134671","https://openalex.org/W7154474197","https://openalex.org/W4416960408","https://openalex.org/W4414510592","https://openalex.org/W4389264617","https://openalex.org/W4392307294","https://openalex.org/W7116838977"],"abstract_inverted_index":{"EEG":[0],"signal":[1],"processing":[2],"is":[3],"crucial":[4],"for":[5,220,237],"diagnosing":[6],"neurological":[7],"diseases,":[8],"such":[9],"as":[10],"epilepsy.":[11],"This":[12],"study":[13],"systematically":[14],"compares":[15],"the":[16,67,99,135,144,176,212,238,243],"performance":[17,147,204],"of":[18,39,122,151,155,163,178,242],"time-domain":[19,213],"and":[20,42,56,64,91,107,129,157,205,229],"time-frequency":[21],"domain":[22],"representations":[23],"in":[24,73,120,168,211],"detecting":[25],"epilepsy":[26,221],"seizures.":[27],"The":[28,53,116,141,232],"data":[29,43],"were":[30,59,70,96,111,118],"divided":[31],"into":[32],"four-second":[33],"windows":[34,65],"at":[35],"a":[36,49,200,206,224],"sampling":[37],"frequency":[38],"256":[40],"Hz,":[41],"leakage":[44],"was":[45],"prevented":[46],"by":[47,61],"applying":[48],"patient-based":[50,233],"discrimination":[51,234],"protocol.":[52],"training,":[54],"validation,":[55],"test":[57],"sets":[58],"separated":[60],"patient":[62,69],"IDs,":[63],"from":[66,189],"same":[68,100],"not":[71],"included":[72],"more":[74],"than":[75],"one":[76],"partition.":[77],"Six":[78],"deep":[79],"learning":[80],"models,":[81,175],"including":[82],"BiLSTM,":[83],"1D-CNN,":[84],"STFT-CNN,":[85],"STFT-CNN":[86,186],"with":[87,93,148,170,185],"attention":[88,94,180],"mechanism,":[89,95],"ResSTFT-CNN,":[90],"ResSTFT-CNN":[92],"evaluated":[97],"under":[98],"training":[101],"conditions.":[102],"AdamW":[103],"optimization,":[104],"label":[105],"smoothing,":[106],"early":[108],"stopping":[109],"algorithms":[110],"applied":[112],"to":[113,191,245],"all":[114],"models.":[115],"models":[117,244],"compared":[119],"terms":[121],"accuracy,":[123,227],"F1-Macro,":[124],"AUROC,":[125],"AUPRC,":[126],"calibration":[127],"metrics,":[128],"inference":[130,159],"rate.":[131],"Results":[132],"showed":[133],"that":[134,197],"1D-CNN":[136,198],"model":[137,142],"exhibited":[138],"significant":[139],"superiority.":[140],"offered":[143],"most":[145],"balanced":[146],"an":[149,153,158,179,217],"accuracy":[150,187],"0.957,":[152],"AUROC":[154],"0.993,":[156],"time":[160],"per":[161],"sample":[162],"0.09":[164],"milliseconds.":[165],"BiLSTM":[166],"came":[167],"second":[169],"0.936":[171],"accuracy.":[172],"For":[173],"STFT-based":[174],"addition":[177],"mechanism":[181],"significantly":[182],"improved":[183],"performance,":[184],"increasing":[188],"0.78":[190],"0.91.":[192],"Model":[193],"complexity":[194],"analyses":[195],"revealed":[196],"strikes":[199],"balance":[201,225],"between":[202,226],"high":[203],"low":[207],"parameter":[208],"count.":[209],"Working":[210],"representation,":[214],"1DCNN":[215],"offers":[216],"optimal":[218],"solution":[219],"detection,":[222],"striking":[223],"speed,":[228],"clinical":[230],"applicability.":[231],"strategy":[235],"allows":[236],"realistic":[239],"generalization":[240],"capacity":[241],"be":[246],"evaluated.":[247]},"counts_by_year":[],"updated_date":"2026-11-12T12:28:57.059296","created_date":"2026-10-03T00:00:00"}
