{"id":"https://openalex.org/W3082394918","doi":"https://doi.org/10.1109/embc44109.2020.9176741","title":"TinySleepNet: An Efficient Deep Learning Model for Sleep Stage Scoring based on Raw Single-Channel EEG","display_name":"TinySleepNet: An Efficient Deep Learning Model for Sleep Stage Scoring based on Raw Single-Channel EEG","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3082394918","doi":"https://doi.org/10.1109/embc44109.2020.9176741","mag":"3082394918","pmid":"https://pubmed.ncbi.nlm.nih.gov/33018069"},"language":"en","primary_location":{"id":"doi:10.1109/embc44109.2020.9176741","is_oa":false,"landing_page_url":"https://doi.org/10.1109/embc44109.2020.9176741","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 42nd Annual International Conference of the IEEE Engineering in Medicine &amp; Biology Society (EMBC)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref","pubmed"],"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/A5018395413","display_name":"Akara Supratak","orcid":"https://orcid.org/0000-0002-6739-7642"},"institutions":[{"id":"https://openalex.org/I25399158","display_name":"Mahidol University","ror":"https://ror.org/01znkr924","country_code":"TH","type":"education","lineage":["https://openalex.org/I25399158"]}],"countries":["TH"],"is_corresponding":false,"raw_author_name":"Akara Supratak","raw_affiliation_strings":["Faculty of Information and Communication Technology (ICT), Mahidol University, Thailand"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Information and Communication Technology (ICT), Mahidol University, Thailand","institution_ids":["https://openalex.org/I25399158"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045081171","display_name":"Yike Guo","orcid":"https://orcid.org/0000-0002-3075-2161"},"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":"Yike Guo","raw_affiliation_strings":["Data Science Institute, Imperial College London, London, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Data Science Institute, Imperial College London, London, UK","institution_ids":["https://openalex.org/I47508984"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":225,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"2020","issue":null,"first_page":"641","last_page":"644"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":1.0,"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":1.0,"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/T10985","display_name":"Sleep and Wakefulness Research","score":0.9979000091552734,"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":0.9961000084877014,"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/overfitting","display_name":"Overfitting","score":0.8645715713500977},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8283513784408569},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7218577265739441},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.6932748556137085},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6711993217468262},{"id":"https://openalex.org/keywords/sleep-stages","display_name":"Sleep Stages","score":0.5472639203071594},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.519212543964386},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.5155853629112244},{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.47889748215675354},{"id":"https://openalex.org/keywords/electroencephalography","display_name":"Electroencephalography","score":0.46827128529548645},{"id":"https://openalex.org/keywords/sleep","display_name":"Sleep (system call)","score":0.4546450078487396},{"id":"https://openalex.org/keywords/raw-data","display_name":"Raw data","score":0.4454602897167206},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.44250476360321045},{"id":"https://openalex.org/keywords/polysomnography","display_name":"Polysomnography","score":0.2423628568649292},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.2366555631160736}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.8645715713500977},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8283513784408569},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7218577265739441},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.6932748556137085},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6711993217468262},{"id":"https://openalex.org/C2910364982","wikidata":"https://www.wikidata.org/wiki/Q35831","display_name":"Sleep Stages","level":4,"score":0.5472639203071594},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.519212543964386},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.5155853629112244},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.47889748215675354},{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.46827128529548645},{"id":"https://openalex.org/C2775841894","wikidata":"https://www.wikidata.org/wiki/Q4683692","display_name":"Sleep (system call)","level":2,"score":0.4546450078487396},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.4454602897167206},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.44250476360321045},{"id":"https://openalex.org/C2778205975","wikidata":"https://www.wikidata.org/wiki/Q1754874","display_name":"Polysomnography","level":3,"score":0.2423628568649292},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2366555631160736},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C118552586","wikidata":"https://www.wikidata.org/wiki/Q7867","display_name":"Psychiatry","level":1,"score":0.0}],"mesh":[{"descriptor_ui":"D000077321","descriptor_name":"Deep Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000077321","descriptor_name":"Deep Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000077321","descriptor_name":"Deep Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D004569","descriptor_name":"Electroencephalography","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D004569","descriptor_name":"Electroencephalography","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D004569","descriptor_name":"Electroencephalography","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D011939","descriptor_name":"Mental Recall","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D011939","descriptor_name":"Mental Recall","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D011939","descriptor_name":"Mental Recall","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D012890","descriptor_name":"Sleep","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D012890","descriptor_name":"Sleep","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D012890","descriptor_name":"Sleep","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D012894","descriptor_name":"Sleep Stages","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D012894","descriptor_name":"Sleep Stages","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D012894","descriptor_name":"Sleep Stages","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false}],"locations_count":4,"locations":[{"id":"doi:10.1109/embc44109.2020.9176741","is_oa":false,"landing_page_url":"https://doi.org/10.1109/embc44109.2020.9176741","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 42nd Annual International Conference of the IEEE Engineering in Medicine &amp; Biology Society (EMBC)","raw_type":"proceedings-article"},{"id":"pmid:33018069","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/33018069","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","raw_type":null},{"id":"pmh:oai:repository.hkust.edu.hk:1783.1-122488","is_oa":false,"landing_page_url":"http://gateway.isiknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=LinksAMR&SrcApp=PARTNER_APP&DestLinkType=FullRecord&DestApp=WOS&KeyUT=000621592200155","pdf_url":null,"source":{"id":"https://openalex.org/S4306401796","display_name":"Rare & Special e-Zone (The Hong Kong University of Science and Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I200769079","host_organization_name":"Hong Kong University of Science and Technology","host_organization_lineage":["https://openalex.org/I200769079"],"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":"Conference paper"},{"id":"mag:3205613450","is_oa":false,"landing_page_url":"https://jglobal.jst.go.jp/en/detail?JGLOBAL_ID=202002276182763513","pdf_url":null,"source":{"id":"https://openalex.org/S4306512817","display_name":"IEEE Conference Proceedings","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":"IEEE Conference Proceedings","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W1983256092","https://openalex.org/W2004093153","https://openalex.org/W2023817852","https://openalex.org/W2125158184","https://openalex.org/W2144691514","https://openalex.org/W2162800060","https://openalex.org/W2525537354","https://openalex.org/W2604096629","https://openalex.org/W2612047884","https://openalex.org/W2912579446","https://openalex.org/W2920016582","https://openalex.org/W2940589930","https://openalex.org/W2964959984","https://openalex.org/W3137695714","https://openalex.org/W3152633770","https://openalex.org/W3166543270","https://openalex.org/W6637373629","https://openalex.org/W6737603859","https://openalex.org/W6763485134","https://openalex.org/W6766742542","https://openalex.org/W6776930347"],"related_works":["https://openalex.org/W4362597605","https://openalex.org/W1574414179","https://openalex.org/W4297676672","https://openalex.org/W3009056573","https://openalex.org/W2922073769","https://openalex.org/W4281702477","https://openalex.org/W2490526372","https://openalex.org/W4298369531","https://openalex.org/W3155135229","https://openalex.org/W3083218341"],"abstract_inverted_index":{"Deep":[0],"learning":[1,103],"has":[2],"become":[3],"popular":[4],"for":[5,117],"automatic":[6,118],"sleep":[7,69,119,185,194],"stage":[8,120],"scoring":[9,121,203],"due":[10],"to":[11,14,30,48,53,60,81,89,111,137,141,232,247],"its":[12],"capability":[13],"extract":[15],"useful":[16],"features":[17],"from":[18,180],"raw":[19,124],"signals.":[20],"Most":[21],"of":[22,32,67,75,130,134,149,184,202,251],"the":[23,41,62,68,93,114,142,169,172,178,182,215,220,233,248],"existing":[24,143],"models,":[25],"however,":[26],"have":[27,36,197],"been":[28],"overengineered":[29],"consist":[31],"many":[33,87],"layers":[34],"or":[35],"introduced":[37],"additional":[38],"steps":[39],"in":[40,92,200],"processing":[42],"pipeline,":[43],"such":[44],"as":[45],"converting":[46],"signals":[47],"spectrogram-based":[49],"images.":[50],"They":[51],"require":[52],"be":[54,82,90,138,166],"trained":[55,139],"on":[56,123,191,236],"a":[57,72,108,131,146,226],"large":[58],"dataset":[59],"prevent":[61,177],"overfitting":[63],"problem":[64],"(but":[65],"most":[66],"datasets":[70,195],"contain":[71],"limited":[73],"amount":[74,148],"class-imbalanced":[76],"data)":[77],"and":[78,107,152,175,205,208,219],"are":[79,86],"difficult":[80],"applied":[83],"(as":[84],"there":[85],"hyperparameters":[88],"configured":[91],"pipeline).":[94],"In":[95],"this":[96],"paper,":[97],"we":[98],"propose":[99],"an":[100],"efficient":[101],"deep":[102],"model,":[104],"named":[105],"TinySleepNet,":[106],"novel":[109],"technique":[110,157],"effectively":[112],"train":[113],"model":[115,128,135,165,179,190,217],"end-to-end":[116],"based":[122],"single-channel":[125],"EEG.":[126],"Our":[127,155],"consists":[129],"less":[132,147],"number":[133,250],"parameters":[136],"compared":[140,231],"ones,":[144],"requiring":[145],"training":[150,156,221],"data":[151,159],"computational":[153],"resources.":[154],"incorporates":[158],"augmentation":[160],"that":[161,196,241],"can":[162,176,244],"make":[163],"our":[164,189,223,242],"more":[167],"robust":[168],"shift":[170],"along":[171],"time":[173],"axis,":[174],"remembering":[181],"sequence":[183],"stages.":[186],"We":[187],"evaluated":[188],"seven":[192],"public":[193],"different":[198,252],"characteristics":[199],"terms":[201],"criteria":[204],"recording":[206],"channels":[207],"environments.":[209],"The":[210],"results":[211],"show":[212],"that,":[213],"with":[214],"same":[216],"architecture":[218],"parameters,":[222],"method":[224,243],"achieves":[225],"similar":[227],"(or":[228],"better)":[229],"performance":[230],"state-of-the-art":[234],"methods":[235],"all":[237],"datasets.":[238,253],"This":[239],"demonstrates":[240],"generalize":[245],"well":[246],"largest":[249]},"counts_by_year":[{"year":2026,"cited_by_count":17},{"year":2025,"cited_by_count":51},{"year":2024,"cited_by_count":55},{"year":2023,"cited_by_count":52},{"year":2022,"cited_by_count":33},{"year":2021,"cited_by_count":16},{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
