{"id":"W4308364587","doi":"10.1109/tnsre.2022.3220372","title":"A Lightweight Segmented Attention Network for Sleep Staging by Fusing Local Characteristics and Adjacent Information","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Key Research and Development Program of China; Huashan Hospital; National Natural Science Foundation of China","keywords":"Computer science; Recurrent neural network; Residual; Artificial intelligence; Sleep (system call); Deep learning; Block (permutation group theory); Feature extraction; Encoder; Artificial neural network; Pattern recognition (psychology); Sleep Stages; Feature (linguistics); Electroencephalography; Polysomnography; Algorithm; Medicine","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":[0.000188439,0.0001376476,0.0001497661,0.0001229294,0.0004440278,0.0001200891,0.00005141942,0.00003236042,0.000003283235],"category_scores_gemma":[0.0000126377,0.0001343966,0.00004670147,0.000163862,0.00003130564,0.0003916252,0.000003702819,0.0001610451,5.139446e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008504982,"about_ca_system_score_gemma":0.000005156837,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001267847,"about_ca_topic_score_gemma":3.906864e-7,"domain_scores_codex":[0.9990124,0.00007182565,0.0003304952,0.0002156906,0.0001749722,0.0001946408],"domain_scores_gemma":[0.9993444,0.0003896808,0.00009199058,0.00008184212,0.00003040124,0.00006166531],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009153882,0.00006644567,0.00008718872,0.000489119,0.00001779186,8.500959e-7,0.001392022,0.8989211,0.07423234,0.0002370098,0.0002449558,0.02421962],"study_design_scores_gemma":[0.00045625,0.0003735906,0.0003715718,0.00007540396,0.00001454379,0.00001957558,0.000413379,0.992604,0.001984294,0.000003995597,0.003515906,0.0001674891],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4719406,0.00004162051,0.5261969,0.0002155813,0.001053747,0.000389485,0.00008374236,0.00007630882,0.000002046727],"genre_scores_gemma":[0.9991857,0.00001005361,0.0004346131,0.00007789185,0.0000416869,0.0001915772,0.00001088941,0.00001426231,0.00003327311],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5272452,"threshold_uncertainty_score":0.5480534,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007308074209092841,"score_gpt":0.2063199209131191,"score_spread":0.1990118467040262,"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."}}