{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001575989,0.0001157783,0.0004071791,0.0004992267,0.00005922689,0.00001052616,0.0001118932,0.00009220371,0.000005863566],"category_scores_gemma":[0.0004528958,0.00006269491,0.0000458453,0.0006912267,0.0001255982,0.00002433831,0.00004265055,0.0005831236,7.33865e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007065579,"about_ca_system_score_gemma":0.000008484677,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002018947,"about_ca_topic_score_gemma":0.0001141005,"domain_scores_codex":[0.9985527,0.0005587854,0.0003873922,0.000183377,0.0001364473,0.0001812632],"domain_scores_gemma":[0.9979802,0.001659584,0.0002288606,0.00006236607,0.00002256302,0.00004644718],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001672866,0.0001183128,0.01411699,0.0001226758,0.0001196135,0.000558707,0.003253295,0.3366569,0.5035599,0.0003233545,0.00003704696,0.1394603],"study_design_scores_gemma":[0.001200511,0.003423774,0.003796888,0.0001005192,0.00007700055,0.0002412053,0.0003543883,0.9865664,0.002315695,0.001799962,0.00004970094,0.00007390963],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.969568,0.0005735674,0.02720798,0.002393865,0.0001513726,0.00008804044,0.000001212632,0.000005052829,0.00001087207],"genre_scores_gemma":[0.9982845,0.0004936104,0.00018519,0.0009043347,0.000123267,0.000001444432,0.00000107964,0.000004671216,0.000001877339],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6499095,"threshold_uncertainty_score":0.2556624,"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."}}