{"id":"W4377029845","doi":"10.1142/s0219519423500653","title":"EPILEPTIC EEG SIGNALS RHYTHMS ANALYSIS IN THE DETECTION OF FOCAL AND NON-FOCAL SEIZURES BASED ON OPTIMISED MACHINE LEARNING AND DEEP NEURAL NETWORK ARCHITECTURE","year":2023,"lang":"en","type":"article","venue":"Journal of Mechanics in Medicine and Biology","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Support vector machine; Artificial intelligence; Pattern recognition (psychology); Electroencephalography; Computer science; Feature extraction; Epileptic seizure; Autoencoder; Epilepsy; Wavelet; Feature (linguistics); Deep learning; Speech recognition; Neuroscience; Psychology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003770792,0.0004995979,0.000336082,0.0003361148,0.0001187726,0.0003138339,0.0003861851,0.0004049568,0.0007852449],"category_scores_gemma":[0.0007657293,0.0001871939,0.0004319489,0.0002614492,0.0001788855,0.0005132826,0.0003208154,0.0004889456,0.0002609454],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002693666,"about_ca_system_score_gemma":0.0003538467,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002606214,"about_ca_topic_score_gemma":0.003170212,"domain_scores_codex":[0.9998577,0.00002444048,0.000008961104,0.00004068136,0.00004987042,0.00001839097],"domain_scores_gemma":[0.999868,0.00004023502,0.00001960388,0.00001003664,0.00005522769,0.000006890376],"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":[0.000232382,0.0001808038,0.003897756,0.0001493843,0.0001240104,0.0001567145,0.00007033326,0.5135936,0.04706696,0.002501691,0.001642388,0.430384],"study_design_scores_gemma":[0.000002201631,0.0000275882,0.0005878925,0.00000443332,0.000006653926,0.0000180875,0.000002864161,0.9967741,0.002153609,0.0002460193,0.0001736491,0.000002799222],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1029195,0.001047808,0.892222,0.0002481726,0.00006509866,0.00006268997,0.00008128623,0.0008637601,0.00248975],"genre_scores_gemma":[0.8704553,0.0005486442,0.1254226,0.00008281674,0.00003704384,0.00006431311,0.0001923122,0.00003712557,0.003159883],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002606214,"threshold_uncertainty_score":0.005182087,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03079440260670533,"score_gpt":0.3057126968473399,"score_spread":0.2749182942406345,"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."}}