{"id":"W3034369844","doi":"10.24963/ijcai.2020/184","title":"GraphSleepNet: Adaptive Spatial-Temporal Graph Convolutional Networks for Sleep Stage Classification","year":2020,"lang":"en","type":"article","venue":"","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":221,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Computer science; Graph; Sleep Stages; Artificial intelligence; Sleep (system call); Convolutional neural network; Electroencephalography; Convolution (computer science); Pattern recognition (psychology); Adjacency matrix; Artificial neural network; Polysomnography; Theoretical computer science; Neuroscience; Psychology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002711526,0.001004833,0.0004162301,0.0007133047,0.0002402275,0.0003921182,0.0009510493,0.0006220594,0.001610738],"category_scores_gemma":[0.00102019,0.0002888272,0.0006900307,0.0007526937,0.000239707,0.0006974908,0.0005308651,0.0007978764,0.0004358517],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007417887,"about_ca_system_score_gemma":0.0008066557,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01655182,"about_ca_topic_score_gemma":0.03435405,"domain_scores_codex":[0.9998958,0.00001707256,0.000005268464,0.00004199819,0.00001752146,0.0000222847],"domain_scores_gemma":[0.9998457,0.00005636938,0.00002481707,0.00002390224,0.00003278416,0.00001644004],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006564514,0.0003425259,0.009620666,0.0002988693,0.0004262044,0.0003277929,0.0001030902,0.368286,0.01791299,0.007218173,0.03989395,0.5549133],"study_design_scores_gemma":[0.00001922173,0.00005318027,0.001744857,0.0000135387,0.0000345695,0.00005554042,0.00001340347,0.9879638,0.002752599,0.00508595,0.002250844,0.0000124313],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1987811,0.004328239,0.7674245,0.00114131,0.0005434995,0.0002316038,0.007636384,0.01422153,0.005691772],"genre_scores_gemma":[0.8143582,0.001531528,0.1590479,0.000642864,0.000121369,0.0002359681,0.01274764,0.0004571327,0.01085738],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01655182,"threshold_uncertainty_score":0.03291094,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07379366460278758,"score_gpt":0.274777640419938,"score_spread":0.2009839758171504,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}