{"id":"https://openalex.org/W4408353716","doi":"https://doi.org/10.1109/icassp49660.2025.10890643","title":"DARN: An Attention-Based Neural Network Using Residual Blocks for Sleep Micro-Events Detection","display_name":"DARN: An Attention-Based Neural Network Using Residual Blocks for Sleep Micro-Events Detection","publication_year":2025,"publication_date":"2025-03-12","ids":{"openalex":"https://openalex.org/W4408353716","doi":"https://doi.org/10.1109/icassp49660.2025.10890643"},"language":"en","primary_location":{"id":"doi:10.1109/icassp49660.2025.10890643","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp49660.2025.10890643","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2025 - 2025 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/A5100455936","display_name":"Fei Wang","orcid":"https://orcid.org/0000-0003-1669-8338"},"institutions":[{"id":"https://openalex.org/I187400657","display_name":"South China Normal University","ror":"https://ror.org/01kq0pv72","country_code":"CN","type":"education","lineage":["https://openalex.org/I187400657"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Fei Wang","raw_affiliation_strings":["South China Normal University,School of Software,Guangzhou,China"],"affiliations":[{"raw_affiliation_string":"South China Normal University,School of Software,Guangzhou,China","institution_ids":["https://openalex.org/I187400657"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026928088","display_name":"Zhuorong Li","orcid":"https://orcid.org/0000-0003-0734-1556"},"institutions":[{"id":"https://openalex.org/I187400657","display_name":"South China Normal University","ror":"https://ror.org/01kq0pv72","country_code":"CN","type":"education","lineage":["https://openalex.org/I187400657"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhuorong Li","raw_affiliation_strings":["South China Normal University,School of Software,Guangzhou,China"],"affiliations":[{"raw_affiliation_string":"South China Normal University,School of Software,Guangzhou,China","institution_ids":["https://openalex.org/I187400657"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5056693758","display_name":"Jingcong Li","orcid":"https://orcid.org/0000-0003-4931-3035"},"institutions":[{"id":"https://openalex.org/I187400657","display_name":"South China Normal University","ror":"https://ror.org/01kq0pv72","country_code":"CN","type":"education","lineage":["https://openalex.org/I187400657"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingcong Li","raw_affiliation_strings":["South China Normal University,School of Software,Guangzhou,China"],"affiliations":[{"raw_affiliation_string":"South China Normal University,School of Software,Guangzhou,China","institution_ids":["https://openalex.org/I187400657"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100455936"],"corresponding_institution_ids":["https://openalex.org/I187400657"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.04861687,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11373","display_name":"Sleep and Work-Related Fatigue","score":0.9599999785423279,"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"}},"topics":[{"id":"https://openalex.org/T11373","display_name":"Sleep and Work-Related Fatigue","score":0.9599999785423279,"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"}},{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.92330002784729,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9164999723434448,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.7349349856376648},{"id":"https://openalex.org/keywords/sleep","display_name":"Sleep (system call)","score":0.6679688692092896},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6001929044723511},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5836578607559204},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3029313087463379},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.08393093943595886}],"concepts":[{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.7349349856376648},{"id":"https://openalex.org/C2775841894","wikidata":"https://www.wikidata.org/wiki/Q4683692","display_name":"Sleep (system call)","level":2,"score":0.6679688692092896},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6001929044723511},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5836578607559204},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3029313087463379},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.08393093943595886},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp49660.2025.10890643","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp49660.2025.10890643","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W46892599","https://openalex.org/W828992678","https://openalex.org/W1690323875","https://openalex.org/W1956450144","https://openalex.org/W1973109217","https://openalex.org/W1982908638","https://openalex.org/W1983256092","https://openalex.org/W1991934169","https://openalex.org/W2083355945","https://openalex.org/W2148263544","https://openalex.org/W2183171129","https://openalex.org/W2184262435","https://openalex.org/W2194775991","https://openalex.org/W2466852614","https://openalex.org/W2583913471","https://openalex.org/W2641667423","https://openalex.org/W2778677359","https://openalex.org/W2886606283","https://openalex.org/W2888256665","https://openalex.org/W2902751862","https://openalex.org/W2903696695","https://openalex.org/W2917266614","https://openalex.org/W2952729290","https://openalex.org/W2994264552","https://openalex.org/W3196018520"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2560215812","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2949601986"],"abstract_inverted_index":{"Sleep":[0],"micro-events":[1,14],"are":[2,79],"crucial":[3],"indicators":[4],"of":[5,27,36,59,64,91,141,146,155,160,169,174],"sleep":[6,13,37,60,121,150,178],"quality":[7],"and":[8,30,39,94,102,133,143,157,166,171],"neurological":[9],"health.":[10],"However,":[11],"traditional":[12],"detectors":[15],"often":[16],"suffer":[17],"from":[18],"low":[19],"precision,":[20],"which":[21],"leads":[22],"to":[23,113],"a":[24,139,153,167],"high":[25],"rate":[26],"false":[28],"positives":[29],"misclassification.":[31],"This":[32],"affects":[33],"the":[34,89,116,120,128,135,192],"reliability":[35],"research":[38],"clinical":[40,196],"diagnosis.":[41],"In":[42],"this":[43],"paper,":[44],"we":[45],"propose":[46],"an":[47,144,158,172],"attention-based":[48],"neural":[49,73],"network":[50,95],"using":[51],"residual":[52,86],"blocks":[53],"named":[54],"DARN":[55],"for":[56,81,97,148,162,176,194],"precise":[57,98],"detection":[58,189],"micro-events.":[61],"It":[62],"consists":[63],"three":[65],"main":[66],"parts:":[67],"1)":[68],"local":[69],"feature":[70,99],"extraction:":[71],"convolutional":[72],"networks":[74],"(CNNs)":[75],"with":[76],"attention":[77],"mechanism":[78],"used":[80],"extracting":[82],"temporal":[83,104],"features;":[84],"2)":[85],"module:":[87],"solving":[88],"problems":[90],"gradient":[92],"vanishing":[93],"degradation,":[96],"representations":[100],"learning;":[101],"3)":[103],"context":[105],"modelling:":[106],"bidirectional":[107],"long":[108],"short-term":[109],"memory":[110],"(Bi-LSTM)":[111],"units":[112],"better":[114,195],"capture":[115],"contextual":[117],"relationships":[118],"in":[119],"electroencephalogram":[122],"(EEG).":[123],"By":[124],"conducting":[125],"experiments":[126],"on":[127],"publicly":[129],"accessible":[130],"datasets":[131],"MASS2":[132],"DREAMS,":[134],"proposed":[136],"architecture":[137],"achieves":[138],"precision":[140,154,168],"0.771":[142],"F1-score":[145,159,173],"0.768":[147],"detecting":[149,163,177],"spindles":[151,179],"(MASS2),":[152,165],"0.632":[156],"0.608":[161],"K-complexes":[164],"0.642":[170],"0.616":[175],"(DREAMS).":[180],"The":[181],"results":[182],"show":[183],"that":[184],"our":[185],"method":[186],"outperforms":[187],"existing":[188],"methods,":[190],"laying":[191],"foundation":[193],"utility.":[197]},"counts_by_year":[],"updated_date":"2025-12-19T19:40:27.379048","created_date":"2025-10-10T00:00:00"}