{"id":"W2805033630","doi":"10.1109/tbme.2018.2872652","title":"Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":462,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"NIHR Oxford Biomedical Research Centre; National Institute for Health and Care Research; Wellcome Trust","keywords":"Joint (building); Computer science; Artificial intelligence; Pattern recognition (psychology); Stage (stratigraphy); Support vector machine; Feature extraction; Statistical classification; Speech recognition; Machine learning; Engineering","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":[0.0004866507,0.001252919,0.00058413,0.0006142576,0.0002801733,0.0004497577,0.001305899,0.00058561,0.001514755],"category_scores_gemma":[0.000903177,0.0003308298,0.0007251322,0.0005318173,0.0002184611,0.0007209905,0.0006104343,0.0009863126,0.0005164798],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007914923,"about_ca_system_score_gemma":0.001043605,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02114963,"about_ca_topic_score_gemma":0.03532552,"domain_scores_codex":[0.9997548,0.00003307428,0.00001351594,0.00009506314,0.00004703198,0.00005652229],"domain_scores_gemma":[0.99981,0.00004391913,0.00003148533,0.00003214148,0.00006603388,0.00001645567],"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":[0.0004901785,0.0003319111,0.02013046,0.0001671398,0.0003409246,0.0002493918,0.0001334982,0.252099,0.01975744,0.007361276,0.01614331,0.6827955],"study_design_scores_gemma":[0.000009689778,0.00004593396,0.002410398,0.00001139029,0.00004252169,0.00004063605,0.00001089507,0.9897912,0.0032895,0.002963542,0.001372033,0.0000122912],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09176603,0.00263177,0.8908916,0.0006741637,0.0003176138,0.0001792425,0.002189637,0.005419562,0.005930336],"genre_scores_gemma":[0.8096663,0.0008472877,0.1769282,0.0003092791,0.0001709164,0.0002223443,0.003511377,0.0001372423,0.008207069],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02114963,"threshold_uncertainty_score":0.04205304,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04655704449554923,"score_gpt":0.279111856722724,"score_spread":0.2325548122271748,"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."}}