{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001098929,0.0001781892,0.0001761154,0.00006218588,0.0001795389,0.00007739745,0.0003202702,0.00008431032,0.0001568185],"category_scores_gemma":[0.00008416742,0.0001561432,0.000137157,0.0002838665,0.0001532695,0.0002059004,0.00006340393,0.0001555941,0.0000309918],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001786037,"about_ca_system_score_gemma":0.00002656256,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004949449,"about_ca_topic_score_gemma":0.00004217794,"domain_scores_codex":[0.9985232,0.00009074251,0.000286875,0.0005690485,0.0002274739,0.0003025957],"domain_scores_gemma":[0.9991649,0.0003150672,0.0001366645,0.0001536481,0.0000722179,0.0001575257],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001970088,0.0006457794,0.01321972,0.0001271358,0.0001493042,0.00003058857,0.001944116,0.03383954,0.2495045,0.6005279,0.06425689,0.03378439],"study_design_scores_gemma":[0.0006906507,0.0004639351,0.002378802,0.000007480596,0.00001077403,0.000002418107,0.0001253501,0.9555874,0.02956776,0.001232367,0.009670491,0.000262575],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03602568,0.00004206474,0.9578179,0.002773778,0.000465986,0.0005889037,0.00008568067,0.0002470071,0.001953058],"genre_scores_gemma":[0.9927335,0.00000741335,0.00230132,0.004215285,0.000295794,0.00005713177,0.00003011496,0.00001990623,0.000339479],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9567079,"threshold_uncertainty_score":0.6367337,"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."}}