{"id":"https://openalex.org/W4415101070","doi":"https://doi.org/10.3390/e27101057","title":"Seizure Type Classification Based on Hybrid Feature Engineering and Mutual Information Analysis Using Electroencephalogram","display_name":"Seizure Type Classification Based on Hybrid Feature Engineering and Mutual Information Analysis Using Electroencephalogram","publication_year":2025,"publication_date":"2025-10-11","ids":{"openalex":"https://openalex.org/W4415101070","doi":"https://doi.org/10.3390/e27101057","pmid":"https://pubmed.ncbi.nlm.nih.gov/41149015"},"language":"en","primary_location":{"id":"doi:10.3390/e27101057","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e27101057","pdf_url":"https://www.mdpi.com/1099-4300/27/10/1057/pdf?version=1760176225","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/1099-4300/27/10/1057/pdf?version=1760176225","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101549831","display_name":"Yao Miao","orcid":"https://orcid.org/0000-0002-2585-1368"},"institutions":[{"id":"https://openalex.org/I4210152380","display_name":"Shenzhen Technology University","ror":"https://ror.org/04qzpec27","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210152380"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yao Miao","raw_affiliation_strings":["College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China"],"raw_orcid":"https://orcid.org/0000-0002-2585-1368","affiliations":[{"raw_affiliation_string":"College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China","institution_ids":["https://openalex.org/I4210152380"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5101549831"],"corresponding_institution_ids":["https://openalex.org/I4210152380"],"apc_list":{"value":2000,"currency":"CHF","value_usd":2165},"apc_paid":{"value":2000,"currency":"CHF","value_usd":2165},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.25008672,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"27","issue":"10","first_page":"1057","last_page":"1057"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9994000196456909,"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":0.9994000196456909,"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/T12647","display_name":"Traditional Chinese Medicine Studies","score":0.9829000234603882,"subfield":{"id":"https://openalex.org/subfields/2707","display_name":"Complementary and alternative 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/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9785000085830688,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/mutual-information","display_name":"Mutual information","score":0.6722000241279602},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6326000094413757},{"id":"https://openalex.org/keywords/electroencephalography","display_name":"Electroencephalography","score":0.5748999714851379},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5234000086784363},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.47999998927116394},{"id":"https://openalex.org/keywords/feature-engineering","display_name":"Feature engineering","score":0.40400001406669617},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4018999934196472},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.38429999351501465}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7455000281333923},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6920999884605408},{"id":"https://openalex.org/C152139883","wikidata":"https://www.wikidata.org/wiki/Q252973","display_name":"Mutual information","level":2,"score":0.6722000241279602},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6326000094413757},{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.5748999714851379},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5234000086784363},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.47999998927116394},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.40400001406669617},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4018999934196472},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.385699987411499},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.38429999351501465},{"id":"https://openalex.org/C2778186239","wikidata":"https://www.wikidata.org/wiki/Q41571","display_name":"Epilepsy","level":2,"score":0.3626999855041504},{"id":"https://openalex.org/C2781140086","wikidata":"https://www.wikidata.org/wiki/Q557945","display_name":"Confusion","level":2,"score":0.3531000018119812},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.33739998936653137},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3273000121116638},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.32670000195503235},{"id":"https://openalex.org/C2779334592","wikidata":"https://www.wikidata.org/wiki/Q6279182","display_name":"Epileptic seizure","level":3,"score":0.3068000078201294},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.2930999994277954},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.29280000925064087},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.28610000014305115},{"id":"https://openalex.org/C96835011","wikidata":"https://www.wikidata.org/wiki/Q1638718","display_name":"Hurst exponent","level":2,"score":0.28540000319480896},{"id":"https://openalex.org/C2781181686","wikidata":"https://www.wikidata.org/wiki/Q4226068","display_name":"Coherence (philosophical gambling strategy)","level":2,"score":0.2727999985218048}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.3390/e27101057","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e27101057","pdf_url":"https://www.mdpi.com/1099-4300/27/10/1057/pdf?version=1760176225","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy","raw_type":"journal-article"},{"id":"pmid:41149015","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/41149015","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy (Basel, Switzerland)","raw_type":null},{"id":"pmh:oai:doaj.org/article:85f7238eb95944d997036d76fca05e5d","is_oa":true,"landing_page_url":"https://doaj.org/article/85f7238eb95944d997036d76fca05e5d","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Entropy, Vol 27, Iss 10, p 1057 (2025)","raw_type":"article"},{"id":"pmh:oai:europepmc.org:11371242","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/12562691","pdf_url":null,"source":{"id":"https://openalex.org/S4306400806","display_name":"Europe PMC (PubMed Central)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1303153112","host_organization_name":"European Bioinformatics Institute","host_organization_lineage":["https://openalex.org/I1303153112"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/e27101057","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e27101057","pdf_url":"https://www.mdpi.com/1099-4300/27/10/1057/pdf?version=1760176225","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320312387","display_name":"Temple University","ror":"https://ror.org/00kx1jb78"}],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4415101070.pdf"},"referenced_works_count":50,"referenced_works":["https://openalex.org/W125360092","https://openalex.org/W1862394037","https://openalex.org/W1978437325","https://openalex.org/W2030925257","https://openalex.org/W2075851722","https://openalex.org/W2081895431","https://openalex.org/W2097171469","https://openalex.org/W2107541057","https://openalex.org/W2118183148","https://openalex.org/W2119234283","https://openalex.org/W2119821739","https://openalex.org/W2122111042","https://openalex.org/W2134050473","https://openalex.org/W2149706766","https://openalex.org/W2149772057","https://openalex.org/W2158698691","https://openalex.org/W2169918686","https://openalex.org/W2295598076","https://openalex.org/W2341760625","https://openalex.org/W2345279893","https://openalex.org/W2592509339","https://openalex.org/W2800428573","https://openalex.org/W2911761011","https://openalex.org/W2911964244","https://openalex.org/W2936479322","https://openalex.org/W2948224565","https://openalex.org/W2963197365","https://openalex.org/W2997591727","https://openalex.org/W3000002325","https://openalex.org/W3030870789","https://openalex.org/W3131375430","https://openalex.org/W3164989905","https://openalex.org/W3165430704","https://openalex.org/W3201326639","https://openalex.org/W4206477528","https://openalex.org/W4206993905","https://openalex.org/W4210547644","https://openalex.org/W4241136696","https://openalex.org/W4293652294","https://openalex.org/W4319295260","https://openalex.org/W4381387796","https://openalex.org/W4389933882","https://openalex.org/W4390827232","https://openalex.org/W4393219400","https://openalex.org/W4403013297","https://openalex.org/W4405717883","https://openalex.org/W4406852780","https://openalex.org/W4411488671","https://openalex.org/W4411554315","https://openalex.org/W4412492677"],"related_works":[],"abstract_inverted_index":{"Epilepsy":[0],"has":[1],"diverse":[2],"seizure":[3,50,174],"types":[4],"that":[5],"challenge":[6],"diagnosis":[7,22],"and":[8,12,30,57,64,98,113,181,189],"treatment,":[9],"requiring":[10],"automated":[11,48],"accurate":[13],"classification":[14,52,176],"to":[15,42,61,108],"improve":[16],"patient":[17],"outcomes.":[18],"Traditional":[19],"electroencephalogram":[20],"(EEG)-based":[21],"relies":[23],"on":[24],"manual":[25],"interpretation,":[26],"which":[27,185],"is":[28],"subjective":[29],"inefficient,":[31],"particularly":[32],"for":[33,47,192],"multi-class":[34,49],"differentiation":[35],"in":[36,168],"imbalanced":[37],"datasets.":[38],"This":[39,171],"study":[40],"aims":[41],"develop":[43],"a":[44,132,187],"hybrid":[45],"framework":[46,172],"type":[51,175],"using":[53],"segment-wise":[54],"EEG":[55,68],"processing":[56],"multi-band":[58,87,179],"feature":[59],"engineering":[60],"enhance":[62],"precision":[63],"address":[65],"data":[66],"challenges.":[67],"signals":[69],"from":[70],"the":[71,84,110,139,144,182],"TUSZ":[72],"dataset":[73],"were":[74,125],"segmented":[75],"into":[76],"1-s":[77],"windows":[78],"with":[79,131,154],"0.5-s":[80],"overlaps,":[81],"followed":[82],"by":[83,177],"extraction":[85],"of":[86],"features,":[88,112],"including":[89],"statistical":[90],"measures,":[91],"sample":[92],"entropy,":[93],"wavelet":[94],"energies,":[95],"Hurst":[96],"exponent,":[97],"Hjorth":[99],"parameters.":[100],"The":[101,136],"mutual":[102],"information":[103],"(MI)":[104],"approach":[105],"was":[106],"employed":[107],"select":[109],"optimal":[111],"seven":[114],"machine":[115],"learning":[116],"models":[117],"(SVM,":[118],"KNN,":[119],"DT,":[120],"RF,":[121],"XGBoost,":[122],"CatBoost,":[123],"LightGBM)":[124],"evaluated":[126],"via":[127],"10-fold":[128],"stratified":[129],"cross-validation":[130],"class":[133],"balancing":[134],"strategy.":[135],"results":[137],"showed":[138],"following:":[140],"(1)":[141],"XGBoost":[142],"achieved":[143],"highest":[145],"performance":[146],"(accuracy:":[147],"0.8710,":[148],"F1":[149],"score:":[150],"0.8721,":[151],"AUC:":[152],"0.9797),":[153],"\u03b3-band":[155],"features":[156,180],"dominating":[157],"importance.":[158],"(2)":[159],"Confusion":[160],"matrices":[161],"indicated":[162],"robust":[163],"discrimination":[164],"but":[165],"noted":[166],"overlaps":[167],"focal":[169],"subtypes.":[170],"advances":[173],"integrating":[178],"MI":[183],"method,":[184],"offers":[186],"scalable":[188],"interpretable":[190],"tool":[191],"supporting":[193],"clinical":[194],"epilepsy":[195],"diagnostics.":[196]},"counts_by_year":[],"updated_date":"2026-06-13T06:13:01.061226","created_date":"2025-10-13T00:00:00"}