{"id":"W3110662188","doi":"10.1109/ehb50910.2020.9280267","title":"Low Latency Automated Epileptic Seizure Detection: Individualized vs. Global Approaches","year":2020,"lang":"en","type":"article","venue":"2020 International Conference on e-Health and Bioengineering (EHB)","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bishop's University","funders":"","keywords":"Latency (audio); Epilepsy; Computer science; Epileptic seizure; Electroencephalography; Artificial neural network; Artificial intelligence; Pattern recognition (psychology); Sensitivity (control systems); Computational complexity theory; Machine learning; Speech recognition; Algorithm; Neuroscience; Psychology; Engineering","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":[0.0001096537,0.0002671904,0.0002725392,0.00007051237,0.0001277688,0.0002158847,0.0004234861,0.00009986115,0.0001110502],"category_scores_gemma":[0.0002114447,0.0002394511,0.0000611118,0.000302253,0.00006955669,0.0001796764,0.0001253399,0.0003113876,0.00007470138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007771411,"about_ca_system_score_gemma":0.0001192684,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001681626,"about_ca_topic_score_gemma":0.000007092301,"domain_scores_codex":[0.9982637,0.0000746697,0.0003697907,0.0005905876,0.0003468609,0.0003543715],"domain_scores_gemma":[0.9992033,0.00006800046,0.0001380746,0.0001262281,0.00005158404,0.0004128377],"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":[0.003429998,0.00154908,0.005928338,0.00368642,0.0006924684,0.0006298831,0.01390848,0.0103278,0.1556553,0.1710542,0.03203544,0.6011026],"study_design_scores_gemma":[0.001077626,0.00119909,0.001154788,0.0002113877,0.000007056814,0.00009917154,0.0001811158,0.9763671,0.01226681,0.000342014,0.006666025,0.0004278839],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8645293,0.0003692214,0.0290618,0.08995997,0.003927158,0.00117993,0.0006627822,0.0032264,0.007083439],"genre_scores_gemma":[0.9936104,0.0001731831,0.0009870529,0.004842034,0.0002677933,0.00001972906,0.00002895335,0.00001623056,0.00005467127],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9660392,"threshold_uncertainty_score":0.9764532,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07075192858293305,"score_gpt":0.2926919140598637,"score_spread":0.2219399854769307,"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."}}