{"id":"https://openalex.org/W4224927319","doi":"https://doi.org/10.1109/icassp43922.2022.9746014","title":"A Novel Unsupervised Autoencoder-Based HFOs Detector in Intracranial EEG Signals","display_name":"A Novel Unsupervised Autoencoder-Based HFOs Detector in Intracranial EEG Signals","publication_year":2022,"publication_date":"2022-04-27","ids":{"openalex":"https://openalex.org/W4224927319","doi":"https://doi.org/10.1109/icassp43922.2022.9746014"},"language":"en","primary_location":{"id":"doi:10.1109/icassp43922.2022.9746014","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp43922.2022.9746014","pdf_url":null,"source":{"id":"https://openalex.org/S4363607702","display_name":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5082702571","display_name":"Weilai Li","orcid":"https://orcid.org/0000-0003-3748-6254"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weilai Li","raw_affiliation_strings":["University of Electronic Science and Technology of China (UESTC),Chengdu,China,611731"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China (UESTC),Chengdu,China,611731","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042832520","display_name":"Lanfeng Zhong","orcid":"https://orcid.org/0009-0004-3570-382X"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lanfeng Zhong","raw_affiliation_strings":["University of Electronic Science and Technology of China (UESTC),Chengdu,China,611731"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China (UESTC),Chengdu,China,611731","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084214869","display_name":"Weixi Xiang","orcid":null},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weixi Xiang","raw_affiliation_strings":["University of Electronic Science and Technology of China (UESTC),Chengdu,China,611731"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China (UESTC),Chengdu,China,611731","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063703257","display_name":"Tongzhou Kang","orcid":"https://orcid.org/0000-0002-6847-3774"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tongzhou Kang","raw_affiliation_strings":["University of Electronic Science and Technology of China (UESTC),Chengdu,China,611731"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China (UESTC),Chengdu,China,611731","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5065376533","display_name":"Dakun Lai","orcid":"https://orcid.org/0000-0001-9070-1721"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dakun Lai","raw_affiliation_strings":["University of Electronic Science and Technology of China (UESTC),Chengdu,China,611731"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China (UESTC),Chengdu,China,611731","institution_ids":["https://openalex.org/I150229711"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I150229711"],"apc_list":null,"apc_paid":null,"fwci":0.3988,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.43397324,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1426","last_page":"1430"},"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/T10581","display_name":"Neural dynamics and brain function","score":0.9991000294685364,"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9970999956130981,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/autoencoder","display_name":"Autoencoder","score":0.8778072595596313},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7526119947433472},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.752289354801178},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6545512676239014},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6288096904754639},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.5363342761993408},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.5287750959396362},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.5195356011390686},{"id":"https://openalex.org/keywords/sensitivity","display_name":"Sensitivity (control systems)","score":0.47855716943740845},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.44198957085609436},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.31698113679885864},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08008503913879395}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.8778072595596313},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7526119947433472},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.752289354801178},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6545512676239014},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6288096904754639},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.5363342761993408},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.5287750959396362},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.5195356011390686},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.47855716943740845},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.44198957085609436},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.31698113679885864},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08008503913879395},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp43922.2022.9746014","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp43922.2022.9746014","pdf_url":null,"source":{"id":"https://openalex.org/S4363607702","display_name":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W634542829","https://openalex.org/W1844684489","https://openalex.org/W1941650629","https://openalex.org/W1959608418","https://openalex.org/W1997629662","https://openalex.org/W2000837205","https://openalex.org/W2000845031","https://openalex.org/W2062706202","https://openalex.org/W2102409316","https://openalex.org/W2104930442","https://openalex.org/W2150374621","https://openalex.org/W2166543843","https://openalex.org/W2167601989","https://openalex.org/W2211435930","https://openalex.org/W2268578292","https://openalex.org/W2558260604","https://openalex.org/W2590272542","https://openalex.org/W2898293270","https://openalex.org/W2979940200","https://openalex.org/W3009693843","https://openalex.org/W3035121082","https://openalex.org/W4206566734","https://openalex.org/W6638775169","https://openalex.org/W6640963894","https://openalex.org/W6675401909"],"related_works":["https://openalex.org/W4310873165","https://openalex.org/W2355395139","https://openalex.org/W4285596704","https://openalex.org/W20047544","https://openalex.org/W2806873178","https://openalex.org/W2965146396","https://openalex.org/W2770818364","https://openalex.org/W281791438","https://openalex.org/W4312416532","https://openalex.org/W4390806283"],"abstract_inverted_index":{"High":[0],"frequency":[1],"oscillations":[2],"(HFOs)":[3],"have":[4],"demonstrated":[5],"their":[6],"potency":[7],"acting":[8],"as":[9,96],"an":[10,48,65],"effective":[11],"biomarker":[12],"in":[13,133,136,140],"epilepsy.":[14],"However,":[15],"most":[16],"of":[17,90,101,106],"the":[18,87,91,99,123],"existing":[19,128],"HFOs":[20,51],"detectors":[21],"are":[22],"based":[23,53],"on":[24,54,86],"manual":[25],"feature":[26,34],"extraction":[27],"and":[28,36,104,130,138],"supervised":[29],"learning,":[30],"which":[31],"incur":[32],"laborious":[33],"selection":[35],"time-consuming":[37],"labeling":[38],"process.":[39],"In":[40],"order":[41],"to":[42,97],"tackle":[43],"these":[44],"issues,":[45],"we":[46],"propose":[47],"automatic":[49],"unsupervised":[50],"detector":[52],"convolutional":[55],"variational":[56],"autoencoder":[57],"(CVAE).":[58],"First,":[59],"each":[60],"selected":[61],"HFO":[62],"candidate":[63],"(via":[64],"initial":[66],"detection":[67],"method)":[68],"is":[69,84,113],"converted":[70],"into":[71],"a":[72],"2-D":[73],"time-frequency":[74],"map":[75],"(TFM)":[76],"using":[77],"continuous":[78],"wavelet":[79],"transform":[80],"(CWT).":[81],"Then,":[82],"CVAE":[83],"trained":[85],"red":[88],"channel":[89],"TFM":[92],"(R-TFM)":[93],"dataset":[94,112],"so":[95],"achieve":[98,131],"goal":[100],"dimensionality":[102],"reduction":[103],"reconstruction":[105],"input":[107],"feature.":[108],"The":[109],"reconstructed":[110],"R-TFM":[111],"later":[114],"classified":[115],"by":[116],"K-means":[117],"algorithm.":[118],"Experimental":[119],"results":[120],"show":[121],"that":[122],"proposed":[124],"method":[125],"outperforms":[126],"four":[127],"detectors,":[129],"92.85%":[132],"accuracy,":[134],"93.91%":[135],"sensitivity,":[137],"92.14%":[139],"specificity.":[141]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":2}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}
