{"id":"https://openalex.org/W7154455543","doi":"https://doi.org/10.48550/arxiv.2604.11971","title":"Classification of Epileptic iEEG using Topological Machine Learning","display_name":"Classification of Epileptic iEEG using Topological Machine Learning","publication_year":2026,"publication_date":"2026-04-13","ids":{"openalex":"https://openalex.org/W7154455543","doi":"https://doi.org/10.48550/arxiv.2604.11971"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.11971","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.11971","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.11971","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5023782229","display_name":"Sunia Tanweer","orcid":"https://orcid.org/0000-0002-7232-0973"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Tanweer, Sunia","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067871457","display_name":"Narayan Puthanmadam Subramaniyam","orcid":"https://orcid.org/0000-0001-6056-9336"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Subramaniyam, Narayan Puthanmadam","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5030913569","display_name":"Firas A. Khasawneh","orcid":"https://orcid.org/0000-0001-7817-7445"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Khasawneh, Firas A.","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5023782229"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"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/T12536","display_name":"Topological and Geometric Data Analysis","score":0.972100019454956,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12536","display_name":"Topological and Geometric Data Analysis","score":0.972100019454956,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/T10241","display_name":"Functional Brain Connectivity Studies","score":0.0052999998442828655,"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/T12923","display_name":"Digital Image Processing Techniques","score":0.00419999985024333,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/overfitting","display_name":"Overfitting","score":0.7164999842643738},{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.6244999766349792},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.5450999736785889},{"id":"https://openalex.org/keywords/topological-data-analysis","display_name":"Topological data analysis","score":0.5105999708175659},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5072000026702881},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.44269999861717224},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4262000024318695},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.40130001306533813}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7178999781608582},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.7164999842643738},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.6244999766349792},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5929999947547913},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.5450999736785889},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5145999789237976},{"id":"https://openalex.org/C2776477805","wikidata":"https://www.wikidata.org/wiki/Q4460773","display_name":"Topological data analysis","level":2,"score":0.5105999708175659},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5072000026702881},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.44269999861717224},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4262000024318695},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40130001306533813},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.38440001010894775},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.34220001101493835},{"id":"https://openalex.org/C151876577","wikidata":"https://www.wikidata.org/wiki/Q7049464","display_name":"Nonlinear dimensionality reduction","level":3,"score":0.3352999985218048},{"id":"https://openalex.org/C17755696","wikidata":"https://www.wikidata.org/wiki/Q16564780","display_name":"Ictal","level":3,"score":0.3212999999523163},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.31929999589920044},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.3118000030517578},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3091999888420105},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.30160000920295715},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.27900001406669617},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.26820001006126404},{"id":"https://openalex.org/C184720557","wikidata":"https://www.wikidata.org/wiki/Q7825049","display_name":"Topology (electrical circuits)","level":2,"score":0.25440001487731934}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.11971","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.11971","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.11971","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.11971","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Epileptic":[0],"seizure":[1],"detection":[2],"from":[3,29,48,57,74],"EEG":[4],"signals":[5,76],"remains":[6],"challenging":[7],"due":[8,178],"to":[9,130,144,150,179,197],"the":[10,36,170,180,187],"high":[11],"dimensionality":[12,113,191],"and":[13,44,88,118],"nonlinear,":[14],"potentially":[15],"stochastic,":[16],"dynamics":[17],"of":[18,38,189],"neural":[19,199],"activity.":[20],"In":[21,166],"this":[22],"work,":[23],"we":[24,102],"investigate":[25],"whether":[26],"features":[27,159],"derived":[28,73],"topological":[30,94,126,158],"data":[31,56],"analysis":[32],"(TDA)":[33],"can":[34,160],"improve":[35],"classification":[37],"brain":[39],"states":[40],"in":[41],"preictal,":[42],"ictal":[43],"interictal":[45],"iEEG":[46,75,110],"recordings":[47],"epilepsy":[49],"patients":[50],"using":[51,79],"multichannel":[52,172,198],"data.":[53,200],"We":[54],"analyze":[55],"55":[58],"patients,":[59],"significantly":[60],"larger":[61],"than":[62],"many":[63],"previous":[64],"studies":[65],"that":[66,124,155],"rely":[67],"on":[68],"patient-specific":[69],"models.":[70],"Persistence":[71],"diagrams":[72],"are":[77],"vectorized":[78],"several":[80],"TDA":[81],"representations,":[82,117],"including":[83],"Carlsson":[84],"coordinates,":[85],"persistence":[86],"images,":[87],"template":[89],"functions.":[90],"To":[91],"understand":[92],"how":[93],"representations":[95,127,196],"interact":[96],"with":[97],"modern":[98],"machine":[99,139],"learning":[100,140,146],"pipelines,":[101],"conduct":[103],"a":[104],"large-scale":[105],"ablation":[106],"study":[107],"across":[108],"multiple":[109],"frequency":[111],"bands,":[112],"reduction":[114,192],"techniques,":[115],"feature":[116,173,182],"classifier":[119],"architectures.":[120],"Our":[121],"experiments":[122],"show":[123],"dimension-reduced":[125],"achieve":[128],"up":[129,149],"80\\%":[131],"balanced":[132,152],"accuracy":[133],"for":[134],"three-class":[135],"classification.":[136],"Interestingly,":[137],"classical":[138],"models":[141],"perform":[142],"comparably":[143],"deep":[145],"models,":[147],"achieving":[148],"79.17\\%":[151],"accuracy,":[153],"suggesting":[154],"carefully":[156],"designed":[157],"substantially":[161],"reduce":[162],"model":[163],"complexity":[164],"requirements.":[165],"contrast,":[167],"pipelines":[168],"preserving":[169],"full":[171],"structure":[174],"exhibit":[175],"severe":[176],"overfitting":[177],"high-dimensional":[181],"space.":[183],"These":[184],"findings":[185],"highlight":[186],"importance":[188],"structure-preserving":[190],"when":[193],"applying":[194],"topology-based":[195]},"counts_by_year":[],"updated_date":"2026-04-16T06:09:31.884825","created_date":"2026-04-16T00:00:00"}