{"id":"https://openalex.org/W2202772695","doi":"https://doi.org/10.1109/bibm.2015.7359914","title":"EEG-based seizure detection using discrete wavelet transform through full-level decomposition","display_name":"EEG-based seizure detection using discrete wavelet transform through full-level decomposition","publication_year":2015,"publication_date":"2015-11-01","ids":{"openalex":"https://openalex.org/W2202772695","doi":"https://doi.org/10.1109/bibm.2015.7359914","mag":"2202772695"},"language":"en","primary_location":{"id":"doi:10.1109/bibm.2015.7359914","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm.2015.7359914","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","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/A5100646800","display_name":"Duo Chen","orcid":"https://orcid.org/0000-0001-7451-7764"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Duo Chen","raw_affiliation_strings":["School of Biological Science & Medical Engineering, Southeast University Nanjing, Jiangsu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Biological Science & Medical Engineering, Southeast University Nanjing, Jiangsu, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101875273","display_name":"Suiren Wan","orcid":"https://orcid.org/0000-0002-6219-1194"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Suiren Wan","raw_affiliation_strings":["School of Biological Science & Medical Engineering, Southeast University Nanjing, Jiangsu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Biological Science & Medical Engineering, Southeast University Nanjing, Jiangsu, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008923050","display_name":"Forrest Sheng Bao","orcid":"https://orcid.org/0000-0002-5722-5337"},"institutions":[{"id":"https://openalex.org/I110152177","display_name":"University of Akron","ror":"https://ror.org/02kyckx55","country_code":"US","type":"education","lineage":["https://openalex.org/I110152177"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Forrest Sheng Bao","raw_affiliation_strings":["Dept. of Electrical & Computer Engineering, University of Akron, Akron, OH, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Electrical & Computer Engineering, University of Akron, Akron, OH, USA","institution_ids":["https://openalex.org/I110152177"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.8325,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.72959421,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"20","issue":null,"first_page":"1596","last_page":"1602"},"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9983999729156494,"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"}},{"id":"https://openalex.org/T11601","display_name":"Neuroscience and Neural Engineering","score":0.9968000054359436,"subfield":{"id":"https://openalex.org/subfields/2804","display_name":"Cellular and Molecular 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/wavelet","display_name":"Wavelet","score":0.779273271560669},{"id":"https://openalex.org/keywords/electroencephalography","display_name":"Electroencephalography","score":0.7789950966835022},{"id":"https://openalex.org/keywords/discrete-wavelet-transform","display_name":"Discrete wavelet transform","score":0.6555477380752563},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6362334489822388},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5917564034461975},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5610292553901672},{"id":"https://openalex.org/keywords/epilepsy","display_name":"Epilepsy","score":0.499619722366333},{"id":"https://openalex.org/keywords/wavelet-transform","display_name":"Wavelet transform","score":0.49657779932022095},{"id":"https://openalex.org/keywords/epileptic-seizure","display_name":"Epileptic seizure","score":0.4602801203727722},{"id":"https://openalex.org/keywords/wavelet-packet-decomposition","display_name":"Wavelet packet decomposition","score":0.4297053813934326},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.414594441652298},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.19394686818122864},{"id":"https://openalex.org/keywords/neuroscience","display_name":"Neuroscience","score":0.07603970170021057}],"concepts":[{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.779273271560669},{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.7789950966835022},{"id":"https://openalex.org/C46286280","wikidata":"https://www.wikidata.org/wiki/Q2414958","display_name":"Discrete wavelet transform","level":4,"score":0.6555477380752563},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6362334489822388},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5917564034461975},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5610292553901672},{"id":"https://openalex.org/C2778186239","wikidata":"https://www.wikidata.org/wiki/Q41571","display_name":"Epilepsy","level":2,"score":0.499619722366333},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.49657779932022095},{"id":"https://openalex.org/C2779334592","wikidata":"https://www.wikidata.org/wiki/Q6279182","display_name":"Epileptic seizure","level":3,"score":0.4602801203727722},{"id":"https://openalex.org/C155777637","wikidata":"https://www.wikidata.org/wiki/Q2736187","display_name":"Wavelet packet decomposition","level":4,"score":0.4297053813934326},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.414594441652298},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.19394686818122864},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.07603970170021057}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm.2015.7359914","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm.2015.7359914","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.550000011920929,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1549187789","https://openalex.org/W1556131344","https://openalex.org/W1964286771","https://openalex.org/W1969044724","https://openalex.org/W1971386783","https://openalex.org/W1973275441","https://openalex.org/W1978437325","https://openalex.org/W1987549688","https://openalex.org/W1995165836","https://openalex.org/W1996183177","https://openalex.org/W2001103857","https://openalex.org/W2006319238","https://openalex.org/W2030925257","https://openalex.org/W2043596210","https://openalex.org/W2045536507","https://openalex.org/W2058464809","https://openalex.org/W2063375506","https://openalex.org/W2077746856","https://openalex.org/W2085131382","https://openalex.org/W2119314811","https://openalex.org/W2129309861","https://openalex.org/W2132984323","https://openalex.org/W2138190513","https://openalex.org/W2147898188","https://openalex.org/W6633205339","https://openalex.org/W6680455596"],"related_works":["https://openalex.org/W4245508182","https://openalex.org/W2001666425","https://openalex.org/W4233511069","https://openalex.org/W2046633342","https://openalex.org/W2380372197","https://openalex.org/W68308810","https://openalex.org/W2053682625","https://openalex.org/W2085792030","https://openalex.org/W1588899229","https://openalex.org/W1967182499"],"abstract_inverted_index":{"Electroencephalogram":[0],"(EEG)":[1],"is":[2,69,204,230],"a":[3,93],"gold":[4],"standard":[5],"in":[6,65,97,149,227],"epilepsy":[7],"diagnosis":[8],"and":[9,183,194,207],"has":[10,27],"been":[11,28],"widely":[12,29],"studied":[13],"for":[14,48,75,196],"epilepsy-related":[15],"signal":[16,87],"classification.":[17],"In":[18,120],"the":[19,44,55,71,76,85,128,165,190],"past":[20],"few":[21],"years,":[22],"discrete":[23],"wavelet":[24,47,59,67,74,82],"transform":[25,68],"(DWT)":[26],"used":[30,148],"to":[31,201,237],"analyze":[32],"epileptic":[33,49,98,131],"EEG.":[34],"However,":[35],"there":[36],"are":[37],"two":[38],"practical":[39],"questions":[40],"unanswered:":[41],"1.":[42],"what":[43,54],"best":[45,221],"mother":[46,73,81,146,171],"EEG":[50,99,132,218],"analysis":[51,100],"is;":[52],"2.":[53],"optimal":[56,72],"level":[57,163,178],"of":[58,111,216,223],"decomposition":[60,159,162,176],"is.":[61],"The":[62,140,220],"main":[63],"challenge":[64],"using":[66,127,232],"selecting":[70],"given":[77],"task,":[78,134],"as":[79,137],"different":[80,90],"applied":[83],"on":[84,102],"same":[86],"may":[88],"produces":[89],"results.":[91],"Such":[92],"problem":[94],"also":[95],"exist":[96],"based":[101],"wavelet.":[103],"Deeper":[104],"DWT":[105],"can":[106],"yield":[107],"more":[108,117,168],"detailed":[109],"depiction":[110],"signals":[112],"but":[113],"it":[114],"requires":[115],"substantially":[116],"computational":[118],"time.":[119],"this":[121,150,228],"paper,":[122],"we":[123],"study":[124,189],"these":[125],"problems,":[126],"most":[129,191],"common":[130],"classification":[133],"seizure":[135,154,197,206,224],"detection,":[136],"an":[138],"example.":[139],"results":[141,203],"show":[142],"that":[143,205],"all":[144,174,212],"7":[145,179],"wavelets":[147],"work":[151],"achieve":[152],"high":[153,158],"detection":[155,166,225],"accuracy":[156,167,181,222],"at":[157],"levels.":[160],"Also,":[161],"effects":[164],"significantly":[169],"than":[170],"wavelets.":[172],"For":[173],"wavelets,":[175],"beyond":[177],"improves":[180],"limitedly":[182],"even":[184],"decreases":[185],"accuracy.":[186],"We":[187],"further":[188],"effective":[192],"bands":[193,215],"features":[195],"detection.":[198],"An":[199],"interpretation":[200],"our":[202],"non-seizure":[208],"EEGs":[209],"differ":[210],"across":[211],"conventional":[213],"frequency":[214],"human":[217],"rhythms.":[219],"achieved":[226],"research":[229],"92.30%":[231],"coif3":[233],"from":[234],"levels":[235],"2":[236],"7.":[238]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":3}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}