{"id":"W3004011254","doi":"10.1109/globalsip45357.2019.8969414","title":"Epileptic Seizure Prediction: A Multi-Scale Convolutional Neural Network Approach","year":2019,"lang":"en","type":"article","venue":"","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Convolutional neural network; Computer science; Epileptic seizure; Artificial intelligence; Feature extraction; Pattern recognition (psychology); Sensitivity (control systems); Feature (linguistics); Artificial neural network; Epilepsy; Electroencephalography; Scale (ratio); Machine learning","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001171975,0.0001532341,0.0001586735,0.00003561167,0.0001273223,0.00007483908,0.0003004916,0.00007940432,0.0005471854],"category_scores_gemma":[0.00001910826,0.0001215781,0.00008404605,0.0002106304,0.00009619589,0.0002266613,0.0001247471,0.000224898,0.0004895944],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002406105,"about_ca_system_score_gemma":0.00002472793,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006364739,"about_ca_topic_score_gemma":0.000002444502,"domain_scores_codex":[0.9985693,0.00009998335,0.0002099225,0.0005048823,0.0002575353,0.0003584428],"domain_scores_gemma":[0.9994227,0.000142322,0.00005272778,0.0002597496,0.00002874614,0.00009378645],"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":[0.0002926303,0.001727164,0.2187441,0.0002306949,0.00007941284,0.00004095012,0.001864507,0.3561319,0.1987124,0.01917918,0.1982243,0.004772841],"study_design_scores_gemma":[0.0007395266,0.0001447894,0.01497011,0.00001470576,0.000006675632,0.0001537669,0.0000571375,0.9724432,0.003765544,0.0001033132,0.00740737,0.000193869],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9212643,0.00007736679,0.03674443,0.0005774719,0.002581688,0.000625975,0.00003439374,0.0005231402,0.03757124],"genre_scores_gemma":[0.975079,0.000002655339,0.006821188,0.001819913,0.00044383,0.00001743215,0.000008324876,0.00001452705,0.01579313],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6163113,"threshold_uncertainty_score":0.6292908,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0312814407626239,"score_gpt":0.2477269568122309,"score_spread":0.216445516049607,"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."}}