{"id":"W2973069650","doi":"10.1109/jbhi.2019.2937558","title":"A Hierarchical Neural Network for Sleep Stage Classification Based on Comprehensive Feature Learning and Multi-Flow Sequence Learning","year":2019,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":92,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Key Research and Development Program of China Stem Cell and Translational Research; China Postdoctoral Science Foundation","keywords":"Computer science; Artificial intelligence; Artificial neural network; Polysomnography; Sleep Stages; Feature (linguistics); Recurrent neural network; Deep learning; Pattern recognition (psychology); Machine learning; Feature extraction; Electroencephalography","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":[0.0007126064,0.0001410424,0.0003277545,0.000138042,0.0002763818,0.00009315662,0.000137736,0.0001110769,0.000005683634],"category_scores_gemma":[0.0001687244,0.0000997414,0.00005562399,0.0001468268,0.0001723214,0.0002024141,0.00002817689,0.00103844,0.000002505659],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004040495,"about_ca_system_score_gemma":0.0001151675,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001404988,"about_ca_topic_score_gemma":4.668821e-7,"domain_scores_codex":[0.9983423,0.0001546326,0.0006692832,0.0001179255,0.0003820596,0.0003337387],"domain_scores_gemma":[0.9980145,0.0008065709,0.0007011917,0.00006352075,0.00008363983,0.0003306532],"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":[0.001584274,0.0004281731,0.01223384,0.004663124,0.00005434034,0.00004217921,0.0115754,0.1748093,0.016102,0.0006119929,0.008169468,0.7697259],"study_design_scores_gemma":[0.001393069,0.002298051,0.001317474,0.0002877887,0.000004454479,0.00007989854,0.0004094519,0.9587173,0.0001067616,0.00002146024,0.03526478,0.00009953574],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8889433,0.000153954,0.09643654,0.01283062,0.001054242,0.0004851707,0.00001980599,0.00003776458,0.00003865223],"genre_scores_gemma":[0.9332645,0.0002575042,0.05644694,0.009522291,0.0003334007,0.000003336899,0.000008362876,0.00001375018,0.0001499241],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7839079,"threshold_uncertainty_score":0.4511562,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08018510914035216,"score_gpt":0.3515470714976601,"score_spread":0.271361962357308,"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."}}