{"id":"https://openalex.org/W4416250436","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228362","title":"Augmentation of EEG and ECG Time Series for Machine Learning Applications: Integrating Changepoint Detection into the iAAFT Surrogates","display_name":"Augmentation of EEG and ECG Time Series for Machine Learning Applications: Integrating Changepoint Detection into the iAAFT Surrogates","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416250436","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228362"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11228362","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228362","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","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/A5095900688","display_name":"Nina Moutonnet","orcid":null},"institutions":[{"id":"https://openalex.org/I47508984","display_name":"Imperial College London","ror":"https://ror.org/041kmwe10","country_code":"GB","type":"education","lineage":["https://openalex.org/I47508984"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Nina Moutonnet","raw_affiliation_strings":["Imperial College,Department of Computing,London,UK"],"affiliations":[{"raw_affiliation_string":"Imperial College,Department of Computing,London,UK","institution_ids":["https://openalex.org/I47508984"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029886401","display_name":"Gregory Scott","orcid":"https://orcid.org/0000-0001-8063-5871"},"institutions":[{"id":"https://openalex.org/I47508984","display_name":"Imperial College London","ror":"https://ror.org/041kmwe10","country_code":"GB","type":"education","lineage":["https://openalex.org/I47508984"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Gregory Scott","raw_affiliation_strings":["Imperial College,Department of Brain Sciences,London,UK"],"affiliations":[{"raw_affiliation_string":"Imperial College,Department of Brain Sciences,London,UK","institution_ids":["https://openalex.org/I47508984"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5046992958","display_name":"D. Mandic","orcid":"https://orcid.org/0000-0001-9149-447X"},"institutions":[{"id":"https://openalex.org/I47508984","display_name":"Imperial College London","ror":"https://ror.org/041kmwe10","country_code":"GB","type":"education","lineage":["https://openalex.org/I47508984"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Danilo P. Mandic","raw_affiliation_strings":["Imperial College London,Department of Electrical and Electronic Engineering,UK"],"affiliations":[{"raw_affiliation_string":"Imperial College London,Department of Electrical and Electronic Engineering,UK","institution_ids":["https://openalex.org/I47508984"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5095900688"],"corresponding_institution_ids":["https://openalex.org/I47508984"],"apc_list":null,"apc_paid":null,"fwci":1.6044,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.87591306,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9106000065803528,"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":0.9106000065803528,"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":0.0722000002861023,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.0017999999690800905,"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/deep-learning","display_name":"Deep learning","score":0.6614000201225281},{"id":"https://openalex.org/keywords/electroencephalography","display_name":"Electroencephalography","score":0.5353000164031982},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5076000094413757},{"id":"https://openalex.org/keywords/precision-and-recall","display_name":"Precision and recall","score":0.4050000011920929},{"id":"https://openalex.org/keywords/recall","display_name":"Recall","score":0.4018999934196472},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.391400009393692}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7714999914169312},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7573000192642212},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6614000201225281},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5825999975204468},{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.5353000164031982},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5076000094413757},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.4050000011920929},{"id":"https://openalex.org/C100660578","wikidata":"https://www.wikidata.org/wiki/Q18733","display_name":"Recall","level":2,"score":0.4018999934196472},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.391400009393692},{"id":"https://openalex.org/C148524875","wikidata":"https://www.wikidata.org/wiki/Q6975395","display_name":"F1 score","level":2,"score":0.33649998903274536},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.3262999951839447},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.2906999886035919},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.28859999775886536},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2849999964237213},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.2630000114440918}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11228362","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228362","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","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":17,"referenced_works":["https://openalex.org/W1974498657","https://openalex.org/W2003554623","https://openalex.org/W2047187509","https://openalex.org/W2085009317","https://openalex.org/W2134050473","https://openalex.org/W2162273778","https://openalex.org/W2559463885","https://openalex.org/W2792434807","https://openalex.org/W2794550444","https://openalex.org/W2909693411","https://openalex.org/W3046296398","https://openalex.org/W3119861781","https://openalex.org/W3137607436","https://openalex.org/W3179656136","https://openalex.org/W4200012906","https://openalex.org/W4360860566","https://openalex.org/W4378983051"],"related_works":[],"abstract_inverted_index":{"The":[0,72,99],"performance":[1,107],"of":[2,13,41,48,54,91,108,123,147,171],"deep":[3,111,158,180],"learning":[4,112,136,159,181],"methods":[5,33],"is":[6,26,102],"dependent":[7],"on":[8,116,140,165,183],"the":[9,14,39,44,52,79,88,92,106,118,124,142,148,166,172,184,190,219,250],"quality":[10],"and":[11,28,120,126,144,150,155,168,186,197,202,207,209,213,221,229,234,239,241,245,261],"quantity":[12],"available":[15],"training":[16],"data,":[17],"in":[18,174],"particular":[19],"for":[20,67],"physiological":[21],"time":[22,70],"series":[23],"data":[24,31,56],"which":[25],"scarce":[27],"noisy.":[29],"Thus,":[30],"augmentation":[32,57],"are":[34,95],"used":[35],"to":[36],"artificially":[37],"increase":[38],"size":[40],"datasets.":[42],"However,":[43],"time-evolving":[45],"statistical":[46],"properties":[47,90],"nonstationary":[49,69],"signals":[50],"prevent":[51],"use":[53],"standard":[55],"techniques.":[58],"To":[59],"this":[60],"end,":[61],"we":[62],"introduce":[63],"a":[64,110,133,157],"novel":[65],"method":[66,73,101,192],"augmenting":[68],"series.":[71],"combines":[74],"offline":[75],"changepoint":[76],"detection":[77,114,138,163],"with":[78],"iterative":[80],"amplitude-adjusted":[81],"Fourier":[82],"transform":[83],"(iAAFT),":[84],"thereby":[85],"ensuring":[86],"that":[87],"time-frequency":[89],"original":[93,119,143,167],"signal":[94],"pre-served":[96],"during":[97],"augmentation.":[98],"proposed":[100,191],"validated":[103],"by":[104,195,200,205,211,227,232,237,243,253,256,259,263],"comparing":[105],"i)":[109],"seizure":[113,137],"algorithm":[115,139,164],"both":[117,141],"augmented":[121,145,169],"versions":[122,146,170],"CHB-MIT":[125,149,185,220],"Siena":[127,151,187,222],"scalp":[128,152],"electroencephalography":[129],"(EEG)":[130],"datasets,":[131,154,188],"ii)":[132],"feature-based":[134,216],"machine":[135],"EEG":[153],"iii)":[156],"atrial":[160],"fibrillation":[161],"(AF)":[162],"Computing":[173],"Cardiology":[175],"Challenge":[176],"2017":[177],"dataset.":[178],"For":[179,215,247],"classification":[182,217],"respectively,":[189,224],"increased":[193,226,252],"accuracy":[194,225,251],"2.2%":[196,228],"4.6%,":[198],"precision":[199,231,255],"2.3%":[201],"3.6%,":[203],"recall":[204,236,258],"2.8%":[206],"2.3%,":[208],"F1":[210,242,262],"4.2%":[212],"4.2%.":[214],"using":[218],"datasets":[223],"0.2%,":[230,235],"4.3%":[233],"5.2%":[238],"0.1%,":[240,260],"5.4%":[244],"0.3%.":[246],"AF":[248],"classification,":[249],"0.5%,":[254],"6%,":[257],"2.8%.":[264]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-11-14T00:00:00"}