{"id":"W2017689092","doi":"10.1016/j.jneumeth.2015.01.022","title":"Learning machines and sleeping brains: Automatic sleep stage classification using decision-tree multi-class support vector machines","year":2015,"lang":"en","type":"article","venue":"Journal of Neuroscience Methods","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":317,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"Université de Lyon; Canada Research Chairs; Fondation Fyssen; Agence Nationale de la Recherche","keywords":"Support vector machine; Computer science; Artificial intelligence; Pattern recognition (psychology); Linear discriminant analysis; Decision tree; Feature selection; Random forest; Machine learning; Cluster analysis; Sleep Stages; Sensitivity (control systems); Feature (linguistics); Class (philosophy); Tree (set theory); Electroencephalography; Polysomnography; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001202837,0.0005002312,0.0005956313,0.0007051175,0.0002779761,0.0006825016,0.000596443,0.0007856966,0.000806354],"category_scores_gemma":[0.003292825,0.000203896,0.0005730166,0.00071098,0.0002003334,0.0005436139,0.0003786982,0.0008337687,0.0003181493],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002403276,"about_ca_system_score_gemma":0.0003850725,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002097452,"about_ca_topic_score_gemma":0.002035982,"domain_scores_codex":[0.9996451,0.0001319058,0.00003156449,0.00007604925,0.00006034523,0.00005504046],"domain_scores_gemma":[0.9988555,0.000779461,0.00008460747,0.00005226911,0.0001922828,0.0000358525],"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":[0.0008954952,0.0004517819,0.01126541,0.0001997284,0.0001604434,0.0001509244,0.0001869689,0.08080339,0.01421899,0.002261589,0.00432548,0.8850799],"study_design_scores_gemma":[0.00001883787,0.00008427554,0.004751924,0.00001852443,0.00002465856,0.00004770226,0.00004388298,0.9900377,0.002370534,0.002265707,0.0003221376,0.00001401872],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3338367,0.002638751,0.6591657,0.0007001142,0.0002660212,0.0001228972,0.0005002523,0.001145065,0.001624454],"genre_scores_gemma":[0.9148967,0.0002852381,0.08351022,0.00006728477,0.00006873883,0.00006824957,0.0003022303,0.00003626167,0.0007651179],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002097452,"threshold_uncertainty_score":0.006361306,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1751237062647207,"score_gpt":0.4330076272604829,"score_spread":0.2578839209957623,"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."}}