{"id":"https://openalex.org/W4392904434","doi":"https://doi.org/10.1109/icassp48485.2024.10448430","title":"Segmented Error Minimisation (Semi) for Robust Training of Deep Learning Models with Non-Linear Shifts in Reference Data","display_name":"Segmented Error Minimisation (Semi) for Robust Training of Deep Learning Models with Non-Linear Shifts in Reference Data","publication_year":2024,"publication_date":"2024-03-18","ids":{"openalex":"https://openalex.org/W4392904434","doi":"https://doi.org/10.1109/icassp48485.2024.10448430"},"language":"en","primary_location":{"id":"doi:10.1109/icassp48485.2024.10448430","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icassp48485.2024.10448430","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2024 - 2024 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/A5033945689","display_name":"Harry J. Davies","orcid":"https://orcid.org/0000-0001-7506-2300"},"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":"Harry J. Davies","raw_affiliation_strings":["Imperial College,London,UK","Imperial College, London, UK"],"affiliations":[{"raw_affiliation_string":"Imperial College,London,UK","institution_ids":["https://openalex.org/I47508984"]},{"raw_affiliation_string":"Imperial College, London, UK","institution_ids":["https://openalex.org/I47508984"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088905360","display_name":"Yuyang Miao","orcid":"https://orcid.org/0000-0002-9812-716X"},"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":"Yuyang Miao","raw_affiliation_strings":["Imperial College,London,UK","Imperial College, London, UK"],"affiliations":[{"raw_affiliation_string":"Imperial College,London,UK","institution_ids":["https://openalex.org/I47508984"]},{"raw_affiliation_string":"Imperial College, London, UK","institution_ids":["https://openalex.org/I47508984"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080959694","display_name":"Amir Nassibi","orcid":"https://orcid.org/0000-0002-2929-0246"},"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":"Amir Nassibi","raw_affiliation_strings":["Imperial College,London,UK","Imperial College, London, UK"],"affiliations":[{"raw_affiliation_string":"Imperial College,London,UK","institution_ids":["https://openalex.org/I47508984"]},{"raw_affiliation_string":"Imperial College, London, UK","institution_ids":["https://openalex.org/I47508984"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5093459977","display_name":"Morteza Khaleghimeybodi","orcid":null},"institutions":[{"id":"https://openalex.org/I4210128585","display_name":"META Health","ror":"https://ror.org/035h67p10","country_code":"US","type":"other","lineage":["https://openalex.org/I4210128585"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Morteza Khaleghimeybodi","raw_affiliation_strings":["Meta Reality Labs Research,USA","Meta Reality Labs Research, USA"],"affiliations":[{"raw_affiliation_string":"Meta Reality Labs Research,USA","institution_ids":["https://openalex.org/I4210128585"]},{"raw_affiliation_string":"Meta Reality Labs Research, USA","institution_ids":["https://openalex.org/I4210128585"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103001848","display_name":"Danilo P. Mandic","orcid":"https://orcid.org/0000-0001-8432-3963"},"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,UK","Imperial College, London, UK"],"affiliations":[{"raw_affiliation_string":"Imperial College,London,UK","institution_ids":["https://openalex.org/I47508984"]},{"raw_affiliation_string":"Imperial College, London, UK","institution_ids":["https://openalex.org/I47508984"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5033945689"],"corresponding_institution_ids":["https://openalex.org/I47508984"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.03131273,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"32","issue":null,"first_page":"5215","last_page":"5219"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11196","display_name":"Non-Invasive Vital Sign Monitoring","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11196","display_name":"Non-Invasive Vital Sign Monitoring","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11700","display_name":"Hemodynamic Monitoring and Therapy","score":0.9984999895095825,"subfield":{"id":"https://openalex.org/subfields/2746","display_name":"Surgery"},"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/T10745","display_name":"Heart Rate Variability and Autonomic Control","score":0.9979000091552734,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.6899887919425964},{"id":"https://openalex.org/keywords/photoplethysmogram","display_name":"Photoplethysmogram","score":0.684497594833374},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6395446062088013},{"id":"https://openalex.org/keywords/minimisation","display_name":"Minimisation (clinical trials)","score":0.6032682657241821},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.5155029892921448},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.49806642532348633},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.46555644273757935},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.46043485403060913},{"id":"https://openalex.org/keywords/mean-absolute-error","display_name":"Mean absolute error","score":0.42601141333580017},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.4215927720069885},{"id":"https://openalex.org/keywords/modalities","display_name":"Modalities","score":0.41341957449913025},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.341838002204895},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.253476619720459},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.24788162112236023},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.13018569350242615}],"concepts":[{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.6899887919425964},{"id":"https://openalex.org/C116390426","wikidata":"https://www.wikidata.org/wiki/Q7187885","display_name":"Photoplethysmogram","level":3,"score":0.684497594833374},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6395446062088013},{"id":"https://openalex.org/C86941820","wikidata":"https://www.wikidata.org/wiki/Q6865391","display_name":"Minimisation (clinical trials)","level":2,"score":0.6032682657241821},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.5155029892921448},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49806642532348633},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.46555644273757935},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.46043485403060913},{"id":"https://openalex.org/C188154048","wikidata":"https://www.wikidata.org/wiki/Q6803609","display_name":"Mean absolute error","level":3,"score":0.42601141333580017},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.4215927720069885},{"id":"https://openalex.org/C2779903281","wikidata":"https://www.wikidata.org/wiki/Q6888026","display_name":"Modalities","level":2,"score":0.41341957449913025},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.341838002204895},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.253476619720459},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.24788162112236023},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.13018569350242615},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C36289849","wikidata":"https://www.wikidata.org/wiki/Q34749","display_name":"Social science","level":1,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp48485.2024.10448430","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icassp48485.2024.10448430","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","score":0.44999998807907104,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W1583837637","https://openalex.org/W2144994235","https://openalex.org/W2294418644","https://openalex.org/W2971347383","https://openalex.org/W3039352388","https://openalex.org/W3082212688","https://openalex.org/W3089500187","https://openalex.org/W4210672933","https://openalex.org/W4295312788","https://openalex.org/W4322490903","https://openalex.org/W4385285628","https://openalex.org/W4389537808","https://openalex.org/W6734312481","https://openalex.org/W6766978945","https://openalex.org/W6767141205","https://openalex.org/W6779988842"],"related_works":["https://openalex.org/W2732360296","https://openalex.org/W1994871954","https://openalex.org/W4297152434","https://openalex.org/W2072858761","https://openalex.org/W1540261775","https://openalex.org/W4239033438","https://openalex.org/W1968279762","https://openalex.org/W2898782655","https://openalex.org/W4310873162","https://openalex.org/W3030478139"],"abstract_inverted_index":{"Time":[0],"series":[1],"regression":[2],"models":[3],"are":[4],"typically":[5],"trained":[6],"using":[7],"the":[8,22,82,94,101,119,143,147],"mean":[9],"squared":[10],"error":[11,105],"(MSE)":[12],"and":[13,63,70,89,128],"thus":[14,48],"rely":[15],"critically":[16],"on":[17],"time-aligned":[18],"reference":[19,154],"data.":[20,136],"However,":[21],"MSE":[23],"loss":[24,110],"is":[25,53,123,138],"often":[26],"inadequate":[27],"when":[28,79],"processing":[29],"real-world":[30,144],"data,":[31],"such":[32,64],"as":[33,36,65],"physiological":[34],"signals,":[35],"misalignment":[37],"between":[38,60],"two":[39],"signals":[40],"can":[41],"cause":[42],"a":[43,108,130,153],"large":[44],"change":[45],"in":[46,142],"MSE,":[47],"severely":[49],"inhibiting":[50],"convergence.":[51],"This":[52],"regularly":[54],"compounded":[55],"by":[56],"time":[57,76,116],"varying":[58],"drifts":[59],"different":[61,86,91],"modalities":[62],"photoplethysmography,":[66],"electrocardiography,":[67],"blood":[68],"pressure":[69],"respiration.":[71],"Indeed,":[72],"these":[73],"exhibit":[74],"fluctuating":[75],"delays":[77,117],"even":[78],"taken":[80],"from":[81],"same":[83],"individual":[84],"across":[85],"body":[87],"positions":[88],"at":[90],"times":[92],"of":[93,103,146,149],"day.":[95],"To":[96],"this":[97],"end,":[98],"we":[99],"introduce":[100],"concept":[102],"segmented":[104],"minimisation":[106],"(SEMI),":[107],"new":[109],"function":[111],"which":[112],"accounts":[113],"for":[114],"differing":[115],"among":[118],"variables.":[120],"The":[121],"SEMI":[122],"examined":[124],"both":[125],"through":[126],"simulations,":[127],"via":[129],"denoising":[131,148],"convolutional":[132],"autoencoder":[133],"with":[134,152],"synthetic":[135],"It":[137],"then":[139],"finally":[140],"verified":[141],"application":[145],"wearable":[150],"photoplethysmography":[151],"signal.":[155]},"counts_by_year":[],"updated_date":"2025-12-21T01:58:51.020947","created_date":"2025-10-10T00:00:00"}
