{"id":"https://openalex.org/W4367728209","doi":"https://doi.org/10.1109/access.2023.3272373","title":"A Deep Neural Network Based Wake-After-Sleep-Onset Time Aware Sleep Apnea Severity Estimation Scheme Using Single-Lead ECG Data","display_name":"A Deep Neural Network Based Wake-After-Sleep-Onset Time Aware Sleep Apnea Severity Estimation Scheme Using Single-Lead ECG Data","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4367728209","doi":"https://doi.org/10.1109/access.2023.3272373"},"language":"en","primary_location":{"id":"doi:10.1109/access.2023.3272373","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3272373","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/10005208/10114401.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/10005208/10114401.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5013067168","display_name":"Dae-Woong Seo","orcid":null},"institutions":[{"id":"https://openalex.org/I31419693","display_name":"Kyungpook National University","ror":"https://ror.org/040c17130","country_code":"KR","type":"education","lineage":["https://openalex.org/I31419693"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Dae-Woong Seo","raw_affiliation_strings":["School of Computer Science and Engineering, Kyungpook National University, Daegu, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Kyungpook National University, Daegu, Republic of Korea","institution_ids":["https://openalex.org/I31419693"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110972020","display_name":"Jeeyoung Kim","orcid":null},"institutions":[{"id":"https://openalex.org/I31419693","display_name":"Kyungpook National University","ror":"https://ror.org/040c17130","country_code":"KR","type":"education","lineage":["https://openalex.org/I31419693"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jeeyoung Kim","raw_affiliation_strings":["Graduate School of Data Science, Kyungpook National University, Daegu, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Graduate School of Data Science, Kyungpook National University, Daegu, Republic of Korea","institution_ids":["https://openalex.org/I31419693"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101653997","display_name":"Ho\u2010Won Lee","orcid":"https://orcid.org/0000-0002-8849-920X"},"institutions":[{"id":"https://openalex.org/I31419693","display_name":"Kyungpook National University","ror":"https://ror.org/040c17130","country_code":"KR","type":"education","lineage":["https://openalex.org/I31419693"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Ho-Won Lee","raw_affiliation_strings":["Department of Neurology, School of Medicine, Kyungpook National University, Daegu, Republic of Korea","Brain Science and Engineering Institute, Kyungpook National University, Daegu, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Department of Neurology, School of Medicine, Kyungpook National University, Daegu, Republic of Korea","institution_ids":["https://openalex.org/I31419693"]},{"raw_affiliation_string":"Brain Science and Engineering Institute, Kyungpook National University, Daegu, Republic of Korea","institution_ids":["https://openalex.org/I31419693"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101626811","display_name":"Young\u2010Kyoon Suh","orcid":"https://orcid.org/0000-0003-3124-2566"},"institutions":[{"id":"https://openalex.org/I31419693","display_name":"Kyungpook National University","ror":"https://ror.org/040c17130","country_code":"KR","type":"education","lineage":["https://openalex.org/I31419693"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Young-Kyoon Suh","raw_affiliation_strings":["School of Computer Science and Engineering, Kyungpook National University, Daegu, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Kyungpook National University, Daegu, Republic of Korea","institution_ids":["https://openalex.org/I31419693"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5013067168"],"corresponding_institution_ids":["https://openalex.org/I31419693"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.7012,"has_fulltext":true,"cited_by_count":4,"citation_normalized_percentile":{"value":0.69155027,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"11","issue":null,"first_page":"43720","last_page":"43732"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10234","display_name":"Obstructive Sleep Apnea Research","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2737","display_name":"Physiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T10234","display_name":"Obstructive Sleep Apnea Research","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2737","display_name":"Physiology"},"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/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9923999905586243,"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/T11373","display_name":"Sleep and Work-Related Fatigue","score":0.9879999756813049,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/polysomnography","display_name":"Polysomnography","score":0.705716073513031},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6117369532585144},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6074435114860535},{"id":"https://openalex.org/keywords/obstructive-sleep-apnea","display_name":"Obstructive sleep apnea","score":0.5982459783554077},{"id":"https://openalex.org/keywords/sleep-apnea","display_name":"Sleep apnea","score":0.5849372148513794},{"id":"https://openalex.org/keywords/apnea","display_name":"Apnea","score":0.5482892394065857},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.5300835371017456},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5269796848297119},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5251034498214722},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5157402157783508},{"id":"https://openalex.org/keywords/data-pre-processing","display_name":"Data pre-processing","score":0.4813721776008606},{"id":"https://openalex.org/keywords/hypopnea","display_name":"Hypopnea","score":0.4575693905353546},{"id":"https://openalex.org/keywords/apnea\u2013hypopnea-index","display_name":"Apnea\u2013hypopnea index","score":0.44428151845932007},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.422201931476593},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.33355939388275146},{"id":"https://openalex.org/keywords/cardiology","display_name":"Cardiology","score":0.20956018567085266},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.20797857642173767}],"concepts":[{"id":"https://openalex.org/C2778205975","wikidata":"https://www.wikidata.org/wiki/Q1754874","display_name":"Polysomnography","level":3,"score":0.705716073513031},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6117369532585144},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6074435114860535},{"id":"https://openalex.org/C2776006263","wikidata":"https://www.wikidata.org/wiki/Q16606552","display_name":"Obstructive sleep apnea","level":2,"score":0.5982459783554077},{"id":"https://openalex.org/C2777935920","wikidata":"https://www.wikidata.org/wiki/Q213600","display_name":"Sleep apnea","level":2,"score":0.5849372148513794},{"id":"https://openalex.org/C2781326671","wikidata":"https://www.wikidata.org/wiki/Q754424","display_name":"Apnea","level":2,"score":0.5482892394065857},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.5300835371017456},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5269796848297119},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5251034498214722},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5157402157783508},{"id":"https://openalex.org/C10551718","wikidata":"https://www.wikidata.org/wiki/Q5227332","display_name":"Data pre-processing","level":2,"score":0.4813721776008606},{"id":"https://openalex.org/C2777711342","wikidata":"https://www.wikidata.org/wiki/Q957217","display_name":"Hypopnea","level":4,"score":0.4575693905353546},{"id":"https://openalex.org/C2776377968","wikidata":"https://www.wikidata.org/wiki/Q618736","display_name":"Apnea\u2013hypopnea index","level":4,"score":0.44428151845932007},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.422201931476593},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33355939388275146},{"id":"https://openalex.org/C164705383","wikidata":"https://www.wikidata.org/wiki/Q10379","display_name":"Cardiology","level":1,"score":0.20956018567085266},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.20797857642173767}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2023.3272373","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3272373","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/10005208/10114401.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:4c0f5d9f04dd4cbe9bc7a7414982f915","is_oa":true,"landing_page_url":"https://doaj.org/article/4c0f5d9f04dd4cbe9bc7a7414982f915","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":"IEEE Access, Vol 11, Pp 43720-43732 (2023)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2023.3272373","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3272373","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/10005208/10114401.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/1","display_name":"No poverty","score":0.6299999952316284}],"awards":[{"id":"https://openalex.org/G1577046994","display_name":null,"funder_award_id":"2018R1A6A1A03025109","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"},{"id":"https://openalex.org/G2584457256","display_name":null,"funder_award_id":"NRF-2018R1A6A1A03025109","funder_id":"https://openalex.org/F4320311687","funder_display_name":"Ministry of Education"},{"id":"https://openalex.org/G2693902825","display_name":null,"funder_award_id":"NRF-2018R1A6A1A03025109","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"},{"id":"https://openalex.org/G3034753964","display_name":null,"funder_award_id":"grant","funder_id":"https://openalex.org/F4320320671","funder_display_name":"National Research Foundation"},{"id":"https://openalex.org/G342704958","display_name":null,"funder_award_id":"funded","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"},{"id":"https://openalex.org/G982292920","display_name":null,"funder_award_id":"NRF-20","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"}],"funders":[{"id":"https://openalex.org/F4320311687","display_name":"Ministry of Education","ror":"https://ror.org/03m01yf64"},{"id":"https://openalex.org/F4320320671","display_name":"National Research Foundation","ror":"https://ror.org/05s0g1g46"},{"id":"https://openalex.org/F4320321272","display_name":"Kyungpook National University","ror":"https://ror.org/040c17130"},{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4367728209.pdf","grobid_xml":"https://content.openalex.org/works/W4367728209.grobid-xml"},"referenced_works_count":45,"referenced_works":["https://openalex.org/W165561681","https://openalex.org/W1970596096","https://openalex.org/W2093349040","https://openalex.org/W2108696608","https://openalex.org/W2118978333","https://openalex.org/W2128284264","https://openalex.org/W2131286109","https://openalex.org/W2152402032","https://openalex.org/W2162800060","https://openalex.org/W2163166459","https://openalex.org/W2164179736","https://openalex.org/W2295598076","https://openalex.org/W2517389691","https://openalex.org/W2525537354","https://openalex.org/W2606313992","https://openalex.org/W2755743117","https://openalex.org/W2758886623","https://openalex.org/W2768348081","https://openalex.org/W2791025763","https://openalex.org/W2800788706","https://openalex.org/W2808922551","https://openalex.org/W2896962617","https://openalex.org/W2923821914","https://openalex.org/W2962858109","https://openalex.org/W2962862931","https://openalex.org/W2972799962","https://openalex.org/W2974597585","https://openalex.org/W2998536486","https://openalex.org/W3003257820","https://openalex.org/W3034824783","https://openalex.org/W3083743275","https://openalex.org/W3127637041","https://openalex.org/W3134182943","https://openalex.org/W3143801205","https://openalex.org/W3158231001","https://openalex.org/W3164324706","https://openalex.org/W3171177453","https://openalex.org/W3173656828","https://openalex.org/W3186028383","https://openalex.org/W4212859008","https://openalex.org/W4224303284","https://openalex.org/W4245160364","https://openalex.org/W6737947904","https://openalex.org/W6745609711","https://openalex.org/W7073858967"],"related_works":["https://openalex.org/W2415962170","https://openalex.org/W2370410718","https://openalex.org/W2361491172","https://openalex.org/W2037309944","https://openalex.org/W2405235165","https://openalex.org/W2383496458","https://openalex.org/W4399751843","https://openalex.org/W2386999378","https://openalex.org/W65074871","https://openalex.org/W2385244159"],"abstract_inverted_index":{"Obstructive":[0],"sleep":[1,10,60,129,146],"apnea":[2,208],"(OSA)":[3],"is":[4,14,41],"a":[5,38,104,119,137,174,236],"prevalent":[6],"yet":[7],"potentially":[8],"severe":[9,259],"disorder.":[11],"Polysomnography":[12],"(PSG)":[13],"most":[15,258],"commonly":[16],"used":[17,234],"to":[18,30,63,125,193,224,252],"assess":[19],"the":[20,56,65,69,81,89,185,201,214,219,257],"severity":[21,107,155],"of":[22,58,71,83,91,176,179,207],"OSA.":[23],"However,":[24],"there":[25],"have":[26],"been":[27],"numerous":[28],"studies":[29],"find":[31],"OSA":[32,106,154,170,246],"patients":[33],"more":[34,242],"effectively":[35,243],"since":[36],"running":[37],"PSG":[39,254],"test":[40],"expensive":[42],"and":[43,87,128,141,161,173,198,264],"time-consuming.":[44],"The":[45],"existing":[46,189],"studies,":[47],"however,":[48],"raise":[49],"four":[50],"major":[51],"concerns,":[52,101],"such":[53],"as":[54,116,118,235],"(i)":[55],"use":[57,70],"inaccurate":[59],"time":[61,263],"data":[62,96,140],"calculate":[64],"apnea-hypopnea":[66],"index,":[67],"(ii)":[68],"poor":[72],"preprocessing":[73],"techniques":[74],"for":[75,226],"real":[76],"patient":[77,153,171],"clinical":[78,228],"datasets,":[79],"(iii)":[80],"lack":[82],"multi-stage":[84],"classification":[85,108,209],"capability,":[86],"(iv)":[88],"absence":[90],"experiments":[92],"on":[93,111,200,255,271],"sufficiently":[94],"large":[95],"sets.":[97],"To":[98],"address":[99],"these":[100],"we":[102,211],"propose":[103],"novel":[105,120],"scheme":[109,187,231],"based":[110],"single-lead":[112],"electrocardiogram":[113],"(ECG)":[114],"data,":[115],"well":[117],"deep":[121],"learning":[122],"model,":[123],"CLNet,":[124,149],"perform":[126,253],"apnea/hypopnea":[127,134],"stage":[130],"classification.":[131],"By":[132],"identifying":[133],"events":[135],"from":[136],"patient\u2019s":[138],"ECG":[139,180],"computing":[142],"AHI":[143],"using":[144,163],"\u201cpure\u201d":[145],"duration":[147],"via":[148],"our":[150,183,227],"method":[151],"improves":[152],"degree":[156],"estimation.":[157],"CLNet":[158,216],"was":[159],"trained":[160],"evaluated":[162],"two":[164],"different":[165],"real-world":[166],"datasets":[167],"containing":[168],"286":[169],"records":[172],"total":[175,196],"2,155":[177],"hours":[178],"data.":[181],"In":[182,205],"experiments,":[184],"proposed":[186,215],"outperforms":[188,218],"approaches":[190],"by":[191,222,241],"up":[192,223],"10%":[194],"in":[195],"accuracy":[197],"AUC":[199],"public":[202],"PhysioNet":[203],"dataset.":[204,229],"terms":[206],"sensitivity,":[210],"show":[212],"that":[213],"model":[217,221],"state-of-the-art":[220],"41.8%":[225],"Our":[230,266],"can":[232],"be":[233,250],"successful,":[237],"high-quality":[238],"pre-screening":[239],"tool":[240],"prioritizing":[244],"prospective":[245],"patients.":[247],"We":[248],"will":[249],"able":[251],"only":[256],"patients,":[260],"saving":[261],"both":[262],"money.":[265],"algorithms":[267],"are":[268],"publicly":[269],"available":[270],"GitHub.":[272]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}