{"id":"https://openalex.org/W2951891415","doi":"https://doi.org/10.1109/tencon.2018.8650491","title":"Deep Neural Networks with Weighted Averaged Overnight Airflow Features for Sleep Apnea-Hypopnea Severity Classification","display_name":"Deep Neural Networks with Weighted Averaged Overnight Airflow Features for Sleep Apnea-Hypopnea Severity Classification","publication_year":2018,"publication_date":"2018-10-01","ids":{"openalex":"https://openalex.org/W2951891415","doi":"https://doi.org/10.1109/tencon.2018.8650491","mag":"2951891415"},"language":"en","primary_location":{"id":"doi:10.1109/tencon.2018.8650491","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tencon.2018.8650491","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"TENCON 2018 - 2018 IEEE Region 10 Conference","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1808.10845","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5009497783","display_name":"Payongkit Lakhan","orcid":"https://orcid.org/0000-0003-2226-654X"},"institutions":[{"id":"https://openalex.org/I4210153049","display_name":"Vidyasirimedhi Institute of Science and Technology","ror":"https://ror.org/053jehz60","country_code":"TH","type":"education","lineage":["https://openalex.org/I4210153049"]}],"countries":["TH"],"is_corresponding":true,"raw_author_name":"Payongkit Lakhan","raw_affiliation_strings":["Bio-inspired Robotics and Neural Engineering Lab, School of Information Science and Technology, Vidyasirimedhi Institute of Science & Technology, Thailand"],"affiliations":[{"raw_affiliation_string":"Bio-inspired Robotics and Neural Engineering Lab, School of Information Science and Technology, Vidyasirimedhi Institute of Science & Technology, Thailand","institution_ids":["https://openalex.org/I4210153049"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050282933","display_name":"Apiwat Ditthapron","orcid":"https://orcid.org/0000-0002-1525-8421"},"institutions":[{"id":"https://openalex.org/I107077323","display_name":"Worcester Polytechnic Institute","ror":"https://ror.org/05ejpqr48","country_code":"US","type":"education","lineage":["https://openalex.org/I107077323"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Apiwat Ditthapron","raw_affiliation_strings":["Computer Department, Worcester Polytechnic Institute, Worcester, MA, USA"],"affiliations":[{"raw_affiliation_string":"Computer Department, Worcester Polytechnic Institute, Worcester, MA, USA","institution_ids":["https://openalex.org/I107077323"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004012466","display_name":"Nannapas Banluesombatkul","orcid":"https://orcid.org/0000-0003-4067-0992"},"institutions":[{"id":"https://openalex.org/I4210153049","display_name":"Vidyasirimedhi Institute of Science and Technology","ror":"https://ror.org/053jehz60","country_code":"TH","type":"education","lineage":["https://openalex.org/I4210153049"]}],"countries":["TH"],"is_corresponding":false,"raw_author_name":"Nannapas Banluesombatkul","raw_affiliation_strings":["Bio-inspired Robotics and Neural Engineering Lab, School of Information Science and Technology, Vidyasirimedhi Institute of Science & Technology, Thailand"],"affiliations":[{"raw_affiliation_string":"Bio-inspired Robotics and Neural Engineering Lab, School of Information Science and Technology, Vidyasirimedhi Institute of Science & Technology, Thailand","institution_ids":["https://openalex.org/I4210153049"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5022621021","display_name":"Theerawit Wilaiprasitporn","orcid":"https://orcid.org/0000-0003-4941-4354"},"institutions":[{"id":"https://openalex.org/I4210153049","display_name":"Vidyasirimedhi Institute of Science and Technology","ror":"https://ror.org/053jehz60","country_code":"TH","type":"education","lineage":["https://openalex.org/I4210153049"]}],"countries":["TH"],"is_corresponding":false,"raw_author_name":"Theerawit Wilaiprasitporn","raw_affiliation_strings":["Bio-inspired Robotics and Neural Engineering Lab, School of Information Science and Technology, Vidyasirimedhi Institute of Science & Technology, Thailand"],"affiliations":[{"raw_affiliation_string":"Bio-inspired Robotics and Neural Engineering Lab, School of Information Science and Technology, Vidyasirimedhi Institute of Science & Technology, Thailand","institution_ids":["https://openalex.org/I4210153049"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5009497783"],"corresponding_institution_ids":["https://openalex.org/I4210153049"],"apc_list":null,"apc_paid":null,"fwci":2.7287,"has_fulltext":false,"cited_by_count":39,"citation_normalized_percentile":{"value":0.90857235,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"0441","last_page":"0445"},"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/T11456","display_name":"Neuroscience of respiration and sleep","score":0.9937000274658203,"subfield":{"id":"https://openalex.org/subfields/2807","display_name":"Endocrine and Autonomic Systems"},"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/T10985","display_name":"Sleep and Wakefulness Research","score":0.9666000008583069,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7096290588378906},{"id":"https://openalex.org/keywords/polysomnography","display_name":"Polysomnography","score":0.6934974193572998},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6844357848167419},{"id":"https://openalex.org/keywords/sleep-apnea","display_name":"Sleep apnea","score":0.6296449303627014},{"id":"https://openalex.org/keywords/hypopnea","display_name":"Hypopnea","score":0.6130266189575195},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5737719535827637},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5656832456588745},{"id":"https://openalex.org/keywords/adaboost","display_name":"AdaBoost","score":0.5103039741516113},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.473875492811203},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.46996021270751953},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.461927592754364},{"id":"https://openalex.org/keywords/binary-classification","display_name":"Binary classification","score":0.46066394448280334},{"id":"https://openalex.org/keywords/receiver-operating-characteristic","display_name":"Receiver operating characteristic","score":0.4480835497379303},{"id":"https://openalex.org/keywords/apnea","display_name":"Apnea","score":0.4089796543121338},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.35695040225982666},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.13650834560394287}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7096290588378906},{"id":"https://openalex.org/C2778205975","wikidata":"https://www.wikidata.org/wiki/Q1754874","display_name":"Polysomnography","level":3,"score":0.6934974193572998},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6844357848167419},{"id":"https://openalex.org/C2777935920","wikidata":"https://www.wikidata.org/wiki/Q213600","display_name":"Sleep apnea","level":2,"score":0.6296449303627014},{"id":"https://openalex.org/C2777711342","wikidata":"https://www.wikidata.org/wiki/Q957217","display_name":"Hypopnea","level":4,"score":0.6130266189575195},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5737719535827637},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5656832456588745},{"id":"https://openalex.org/C141404830","wikidata":"https://www.wikidata.org/wiki/Q2823869","display_name":"AdaBoost","level":3,"score":0.5103039741516113},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.473875492811203},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.46996021270751953},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.461927592754364},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.46066394448280334},{"id":"https://openalex.org/C58471807","wikidata":"https://www.wikidata.org/wiki/Q327120","display_name":"Receiver operating characteristic","level":2,"score":0.4480835497379303},{"id":"https://openalex.org/C2781326671","wikidata":"https://www.wikidata.org/wiki/Q754424","display_name":"Apnea","level":2,"score":0.4089796543121338},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.35695040225982666},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.13650834560394287}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tencon.2018.8650491","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tencon.2018.8650491","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"TENCON 2018 - 2018 IEEE Region 10 Conference","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1808.10845","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1808.10845","pdf_url":"https://arxiv.org/pdf/1808.10845","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1808.10845","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1808.10845","pdf_url":"https://arxiv.org/pdf/1808.10845","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1479317506","https://openalex.org/W1513495705","https://openalex.org/W1545241942","https://openalex.org/W1728842521","https://openalex.org/W1966507238","https://openalex.org/W1985854967","https://openalex.org/W1997139643","https://openalex.org/W2028396484","https://openalex.org/W2044404868","https://openalex.org/W2061672958","https://openalex.org/W2070050199","https://openalex.org/W2077410237","https://openalex.org/W2088245374","https://openalex.org/W2110533744","https://openalex.org/W2112458829","https://openalex.org/W2124630796","https://openalex.org/W2129435243","https://openalex.org/W2129525244","https://openalex.org/W2144194006","https://openalex.org/W2155758218","https://openalex.org/W2344257056","https://openalex.org/W2558942501","https://openalex.org/W2592857683","https://openalex.org/W6637572315"],"related_works":["https://openalex.org/W65074871","https://openalex.org/W2385244159","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/W4224267962","https://openalex.org/W2386999378"],"abstract_inverted_index":{"Dramatic":[0],"raising":[1],"of":[2,19,31,56,131,218,237],"Deep":[3,69],"Learning":[4],"(DL)":[5],"approach":[6],"and":[7,65,93,120,166,193,196,221],"its":[8],"capability":[9],"in":[10,74,184,223],"biomedical":[11],"applications":[12],"lead":[13],"us":[14],"to":[15,72,107,136,161],"explore":[16],"the":[17,29,85,97,147,157,163,171,188,194,201,216,228],"advantages":[18],"using":[20,34],"DL":[21,46],"for":[22],"sleep":[23,149],"Apnea-Hypopnea":[24,100],"severity":[25,103],"classification.":[26],"To":[27],"reduce":[28],"complexity":[30],"clinical":[32],"diagnosis":[33],"Polysomnography":[35],"(PSG),":[36],"which":[37,83,140,181],"is":[38],"multiple":[39,98],"sensing":[40,53],"platform,":[41],"we":[42,78,126,169],"incorporates":[43],"our":[44,174,204],"proposed":[45,79,106,205],"scheme":[47],"into":[48,68,110],"one":[49],"single":[50],"Airflow":[51],"(AF)":[52],"signal":[54],"(subset":[55],"PSG).":[57],"Seventeen":[58],"features":[59],"have":[60],"been":[61],"extracted":[62],"from":[63,146,173],"AF":[64,143],"then":[66,155],"fed":[67],"Neural":[70],"Networks":[71],"classify":[73,108],"two":[75,179,213],"studies.":[76],"First,":[77],"a":[80,128,234],"binary":[81,202],"classifications":[82],"use":[84],"cutoff":[86],"indices":[87],"at":[88],"AHI":[89],"=":[90],"5,":[91],"15":[92],"30":[94],"events/hour.":[95],"Second,":[96],"Sleep":[99],"Syndrome":[101],"(SAHS)":[102],"classification":[104],"was":[105],"patients":[109,132],"4":[111],"groups":[112],"including":[113,187],"no":[114],"SAHS,":[115,117,119],"mild":[116],"moderate":[118],"severe":[121],"SAHS.":[122],"For":[123,227],"methods":[124],"evaluation,":[125],"used":[127,183],"higher":[129,209],"number":[130],"than":[133,211],"related":[134],"works":[135],"accommodate":[137],"more":[138],"diversity":[139],"includes":[141],"520":[142],"records":[144],"obtained":[145],"MrOS":[148],"study":[150],"(Visit":[151],"2)":[152],"database.":[153],"We":[154],"applied":[156],"10-fold":[158],"cross-validation":[159],"technique":[160],"get":[162],"accuracy,":[164],"sensitivity":[165],"specificity.":[167],"Moreover,":[168],"compared":[170],"results":[172],"main":[175],"classifier":[176],"with":[177,215,240],"other":[178,212],"approaches":[180,214,239],"were":[182],"previous":[185],"researches":[186],"Support":[189],"Vector":[190],"Machine":[191],"(SVM)":[192],"Adaboost-Classification":[195],"Regression":[197],"Trees":[198],"(AB-CART).":[199],"From":[200],"classification,":[203,230],"method":[206],"provides":[207],"significantly":[208],"performance":[210],"accuracy":[217,236],"83.46%,":[219],"85.39%":[220],"92.69%":[222],"each":[224],"cutoff,":[225],"respectively.":[226],"multiclass":[229],"it":[231],"also":[232],"returns":[233],"highest":[235],"all":[238],"63.70%.":[241]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":12},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":3}],"updated_date":"2026-03-10T16:38:18.471706","created_date":"2019-06-27T00:00:00"}