{"id":"https://openalex.org/W4415683086","doi":"https://doi.org/10.3390/a18110688","title":"Online Imputation of Corrupted Glucose Sensor Data Using Deep Neural Networks and Physiological Inputs","display_name":"Online Imputation of Corrupted Glucose Sensor Data Using Deep Neural Networks and Physiological Inputs","publication_year":2025,"publication_date":"2025-10-29","ids":{"openalex":"https://openalex.org/W4415683086","doi":"https://doi.org/10.3390/a18110688"},"language":"en","primary_location":{"id":"doi:10.3390/a18110688","is_oa":true,"landing_page_url":"https://doi.org/10.3390/a18110688","pdf_url":null,"source":{"id":"https://openalex.org/S190629608","display_name":"Algorithms","issn_l":"1999-4893","issn":["1999-4893"],"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":"Algorithms","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.3390/a18110688","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5040500740","display_name":"Oscar D. S\u00e1nchez","orcid":"https://orcid.org/0000-0001-8215-6348"},"institutions":[{"id":"https://openalex.org/I130040446","display_name":"Universidad Aut\u00f3noma de Guadalajara","ror":"https://ror.org/05ppk0267","country_code":"MX","type":"education","lineage":["https://openalex.org/I130040446"]}],"countries":["MX"],"is_corresponding":true,"raw_author_name":"Oscar D. Sanchez","raw_affiliation_strings":["Departamento Acad\u00e9mico de Computaci\u00f3n e Industrial, Universidad Aut\u00f3noma de Guadalajara, Av. Patria 1201, Zapopan 45129, Mexico"],"affiliations":[{"raw_affiliation_string":"Departamento Acad\u00e9mico de Computaci\u00f3n e Industrial, Universidad Aut\u00f3noma de Guadalajara, Av. Patria 1201, Zapopan 45129, Mexico","institution_ids":["https://openalex.org/I130040446"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5119058570","display_name":"Eduardo Mendez-Palos","orcid":null},"institutions":[{"id":"https://openalex.org/I193181351","display_name":"Universidad de Guadalajara","ror":"https://ror.org/043xj7k26","country_code":"MX","type":"education","lineage":["https://openalex.org/I193181351"]}],"countries":["MX"],"is_corresponding":false,"raw_author_name":"Eduardo Mendez-Palos","raw_affiliation_strings":["Centro Universitario de Ciencias Exactas e Ingenier\u00edas, Universidad de Guadalajara, Blvd. Marcelino Garc\u00eda Barrag\u00e1n 1421, Guadalajara 44430, Mexico"],"affiliations":[{"raw_affiliation_string":"Centro Universitario de Ciencias Exactas e Ingenier\u00edas, Universidad de Guadalajara, Blvd. Marcelino Garc\u00eda Barrag\u00e1n 1421, Guadalajara 44430, Mexico","institution_ids":["https://openalex.org/I193181351"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120183053","display_name":"Daniel Alexander Pascoe","orcid":null},"institutions":[{"id":"https://openalex.org/I193181351","display_name":"Universidad de Guadalajara","ror":"https://ror.org/043xj7k26","country_code":"MX","type":"education","lineage":["https://openalex.org/I193181351"]}],"countries":["MX"],"is_corresponding":false,"raw_author_name":"Daniel A. Pascoe","raw_affiliation_strings":["Centro Universitario de Ciencias Exactas e Ingenier\u00edas, Universidad de Guadalajara, Blvd. Marcelino Garc\u00eda Barrag\u00e1n 1421, Guadalajara 44430, Mexico"],"affiliations":[{"raw_affiliation_string":"Centro Universitario de Ciencias Exactas e Ingenier\u00edas, Universidad de Guadalajara, Blvd. Marcelino Garc\u00eda Barrag\u00e1n 1421, Guadalajara 44430, Mexico","institution_ids":["https://openalex.org/I193181351"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Hannia M. Hernandez","orcid":null},"institutions":[{"id":"https://openalex.org/I193181351","display_name":"Universidad de Guadalajara","ror":"https://ror.org/043xj7k26","country_code":"MX","type":"education","lineage":["https://openalex.org/I193181351"]}],"countries":["MX"],"is_corresponding":false,"raw_author_name":"Hannia M. Hernandez","raw_affiliation_strings":["Centro Universitario de Ciencias Exactas e Ingenier\u00edas, Universidad de Guadalajara, Blvd. Marcelino Garc\u00eda Barrag\u00e1n 1421, Guadalajara 44430, Mexico"],"affiliations":[{"raw_affiliation_string":"Centro Universitario de Ciencias Exactas e Ingenier\u00edas, Universidad de Guadalajara, Blvd. Marcelino Garc\u00eda Barrag\u00e1n 1421, Guadalajara 44430, Mexico","institution_ids":["https://openalex.org/I193181351"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027310548","display_name":"Jes\u00fas Gerardo Cruz \u00c1lvarez","orcid":"https://orcid.org/0000-0003-2451-5925"},"institutions":[{"id":"https://openalex.org/I193181351","display_name":"Universidad de Guadalajara","ror":"https://ror.org/043xj7k26","country_code":"MX","type":"education","lineage":["https://openalex.org/I193181351"]}],"countries":["MX"],"is_corresponding":false,"raw_author_name":"Jesus G. Alvarez","raw_affiliation_strings":["Centro Universitario de Ciencias Exactas e Ingenier\u00edas, Universidad de Guadalajara, Blvd. Marcelino Garc\u00eda Barrag\u00e1n 1421, Guadalajara 44430, Mexico"],"affiliations":[{"raw_affiliation_string":"Centro Universitario de Ciencias Exactas e Ingenier\u00edas, Universidad de Guadalajara, Blvd. Marcelino Garc\u00eda Barrag\u00e1n 1421, Guadalajara 44430, Mexico","institution_ids":["https://openalex.org/I193181351"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5004231492","display_name":"Alma Y. Alan\u00eds","orcid":"https://orcid.org/0000-0001-9600-779X"},"institutions":[{"id":"https://openalex.org/I193181351","display_name":"Universidad de Guadalajara","ror":"https://ror.org/043xj7k26","country_code":"MX","type":"education","lineage":["https://openalex.org/I193181351"]}],"countries":["MX"],"is_corresponding":false,"raw_author_name":"Alma Y. Alanis","raw_affiliation_strings":["Centro Universitario de Ciencias Exactas e Ingenier\u00edas, Universidad de Guadalajara, Blvd. Marcelino Garc\u00eda Barrag\u00e1n 1421, Guadalajara 44430, Mexico"],"affiliations":[{"raw_affiliation_string":"Centro Universitario de Ciencias Exactas e Ingenier\u00edas, Universidad de Guadalajara, Blvd. Marcelino Garc\u00eda Barrag\u00e1n 1421, Guadalajara 44430, Mexico","institution_ids":["https://openalex.org/I193181351"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5040500740"],"corresponding_institution_ids":["https://openalex.org/I130040446"],"apc_list":{"value":1400,"currency":"CHF","value_usd":1515},"apc_paid":{"value":1400,"currency":"CHF","value_usd":1515},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.34389229,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"18","issue":"11","first_page":"688","last_page":"688"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.13760000467300415,"subfield":{"id":"https://openalex.org/subfields/3605","display_name":"Health Information Management"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.13760000467300415,"subfield":{"id":"https://openalex.org/subfields/3605","display_name":"Health Information Management"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10560","display_name":"Diabetes Management and Research","score":0.0989999994635582,"subfield":{"id":"https://openalex.org/subfields/2712","display_name":"Endocrinology, Diabetes and Metabolism"},"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/T11324","display_name":"Spectroscopy Techniques in Biomedical and Chemical Research","score":0.07329999655485153,"subfield":{"id":"https://openalex.org/subfields/1304","display_name":"Biophysics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/imputation","display_name":"Imputation (statistics)","score":0.5666999816894531},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5170000195503235},{"id":"https://openalex.org/keywords/autoregressive-integrated-moving-average","display_name":"Autoregressive integrated moving average","score":0.5034000277519226},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4991999864578247},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4575999975204468},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4551999866962433},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.44369998574256897},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.40130001306533813},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.40119999647140503}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7394000291824341},{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.5666999816894531},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5246999859809875},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5181999802589417},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5170000195503235},{"id":"https://openalex.org/C24338571","wikidata":"https://www.wikidata.org/wiki/Q2566298","display_name":"Autoregressive integrated moving average","level":3,"score":0.5034000277519226},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4991999864578247},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4575999975204468},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4551999866962433},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.44369998574256897},{"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/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.40119999647140503},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.3982999920845032},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.37959998846054077},{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.37560001015663147},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3675000071525574},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.36419999599456787},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.32249999046325684},{"id":"https://openalex.org/C193519340","wikidata":"https://www.wikidata.org/wiki/Q891179","display_name":"Data loss","level":2,"score":0.320499986410141},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.27570000290870667},{"id":"https://openalex.org/C104122410","wikidata":"https://www.wikidata.org/wiki/Q1416406","display_name":"Network model","level":2,"score":0.27079999446868896},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.2612999975681305},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.2542000114917755},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.2517000138759613},{"id":"https://openalex.org/C74883015","wikidata":"https://www.wikidata.org/wiki/Q290467","display_name":"Autoregressive\u2013moving-average model","level":3,"score":0.25110000371932983},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.2500999867916107}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3390/a18110688","is_oa":true,"landing_page_url":"https://doi.org/10.3390/a18110688","pdf_url":null,"source":{"id":"https://openalex.org/S190629608","display_name":"Algorithms","issn_l":"1999-4893","issn":["1999-4893"],"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":"Algorithms","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:5b1d821db2ec4d65978a26da050ce0f4","is_oa":true,"landing_page_url":"https://doaj.org/article/5b1d821db2ec4d65978a26da050ce0f4","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Algorithms, Vol 18, Iss 11, p 688 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/a18110688","is_oa":true,"landing_page_url":"https://doi.org/10.3390/a18110688","pdf_url":null,"source":{"id":"https://openalex.org/S190629608","display_name":"Algorithms","issn_l":"1999-4893","issn":["1999-4893"],"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":"Algorithms","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1986495510","https://openalex.org/W2001875222","https://openalex.org/W2088683982","https://openalex.org/W2158599626","https://openalex.org/W2171613573","https://openalex.org/W2963123914","https://openalex.org/W2963608065","https://openalex.org/W2991326283","https://openalex.org/W3007075806","https://openalex.org/W3083789190","https://openalex.org/W3157175643","https://openalex.org/W3175738155","https://openalex.org/W3184561879","https://openalex.org/W4304987464","https://openalex.org/W4312053133","https://openalex.org/W4320713041","https://openalex.org/W4321021857","https://openalex.org/W4321786619","https://openalex.org/W4367057034","https://openalex.org/W4385763767","https://openalex.org/W4400910020","https://openalex.org/W4401403126","https://openalex.org/W4402439622","https://openalex.org/W4404691738","https://openalex.org/W4405002166"],"related_works":[],"abstract_inverted_index":{"One":[0],"of":[1,17,45,64,86,173,178,193,199],"the":[2,15,43,62,130,134,160,164,183,187,205,218],"main":[3],"challenges":[4],"when":[5],"working":[6],"with":[7,129,190,212],"time":[8],"series":[9],"captured":[10],"online":[11,82],"using":[12,89,144],"sensors":[13],"is":[14,142,163],"appearance":[16],"noise":[18],"or":[19,27,104],"null":[20],"values,":[21],"generally":[22],"caused":[23],"by":[24,59],"sensor":[25,214],"failures":[26],"temporary":[28],"disconnections.":[29],"These":[30],"errors":[31],"compromise":[32],"data":[33,66,88,181,211],"reliability":[34],"and":[35,84,93,119,127,133,149,175,196,224],"can":[36,67],"lead":[37],"to":[38,72],"incorrect":[39],"decisions.":[40],"Particularly":[41],"in":[42,99],"treatment":[44],"diabetes":[46],"mellitus,":[47],"where":[48],"medical":[49],"decisions":[50],"depend":[51],"on":[52,204],"continuous":[53],"glucose":[54,101,106,228],"monitoring":[55,102],"(CGM)":[56],"systems":[57,103],"provided":[58],"modern":[60],"sensors,":[61],"presence":[63],"corrupted":[65],"pose":[68],"a":[69,122],"significant":[70],"risk":[71],"patient":[73],"health.":[74],"This":[75],"work":[76],"presents":[77],"an":[78,171,176,191,197],"approach":[79],"that":[80,159,217],"encompasses":[81],"detection":[83],"imputation":[85],"anomalous":[87],"physiological":[90],"inputs":[91],"(insulin":[92],"carbohydrate":[94],"intake),":[95],"which":[96],"enables":[97],"decision-making":[98],"automatic":[100],"for":[105,167],"control":[107],"purposes.":[108],"Four":[109],"deep":[110],"neural":[111],"network":[112,162,185],"architectures":[113],"are":[114],"proposed:":[115],"CNN-LSTM,":[116],"GRU,":[117],"1D-CNN,":[118],"Transformer-LSTM,":[120],"under":[121,230],"controlled":[123],"fault":[124,168],"injection":[125],"protocol":[126],"compared":[128,143],"ARIMA":[131],"model":[132,220],"Temporal":[135],"Convolutional":[136],"Network":[137],"(TCN).":[138],"The":[139],"obtained":[140,186],"performance":[141],"regression":[145],"(MAE,":[146],"RMSE,":[147],"MARD)":[148],"classification":[150],"(accuracy,":[151],"precision,":[152],"recall,":[153],"F1-score,":[154],"AUC)":[155],"metrics.":[156],"Results":[157],"show":[158],"CNN-LSTM":[161],"most":[165],"effective":[166],"detection,":[169],"achieving":[170],"F1-score":[172],"0.876":[174],"accuracy":[177],"0.979.":[179],"Regarding":[180],"imputation,":[182],"1D-CNN":[184],"best":[188],"performance,":[189],"MAE":[192],"2.96":[194],"mg/dL":[195],"RMSE":[198],"3.75":[200],"mg/dL.":[201],"Then,":[202],"validation":[203],"OhioT1DM":[206],"dataset,":[207],"containing":[208],"real":[209],"CGM":[210],"natural":[213],"disconnections,":[215],"showed":[216],"CNN\u2013LSTM":[219],"accurately":[221],"detected":[222],"anomalies":[223],"reliably":[225],"imputed":[226],"missing":[227],"segments":[229],"real-world":[231],"conditions.":[232]},"counts_by_year":[],"updated_date":"2026-04-17T18:11:37.981687","created_date":"2025-10-30T00:00:00"}
