{"id":"W1733363676","doi":"10.1016/j.eswa.2015.04.068","title":"An automatic mobile-health based approach for EEG epileptic seizures detection","year":2015,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":62,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"Menofia University; United Arab Emirates University; National Research Foundation; Utah Agricultural Experiment Station","keywords":"Computer science; Electroencephalography; Artificial intelligence; Epileptic seizure; Feature selection; Classifier (UML); Scalability; Machine learning; Pattern recognition (psychology); Feature extraction; Data mining; Database","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":[0.0003312007,0.000188627,0.0002491859,0.0001169375,0.0003256423,0.0001587368,0.0003715651,0.00006569032,0.000002276058],"category_scores_gemma":[0.00002880191,0.0001446772,0.00004268683,0.0003207736,0.00007718455,0.0001927377,0.00001554348,0.00009030777,0.00002213249],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001295276,"about_ca_system_score_gemma":0.0001780571,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001262659,"about_ca_topic_score_gemma":0.00001094762,"domain_scores_codex":[0.9983109,0.0001868883,0.0003398303,0.0005726981,0.0002811415,0.0003085265],"domain_scores_gemma":[0.9985965,0.0001803455,0.000213522,0.0006330967,0.00009962055,0.0002769141],"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":[0.0003672416,0.004652204,0.0003925012,0.002688993,0.0001109173,0.000007067441,0.02175364,0.3709441,0.3490077,0.01325716,0.02352348,0.213295],"study_design_scores_gemma":[0.0006807153,0.001128523,0.00001778594,0.00004600374,0.000006559131,0.00007467036,0.001485927,0.943991,0.01758039,0.00004164114,0.03468341,0.0002633661],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01322215,0.0003026123,0.9805539,0.0001064448,0.0001537344,0.004779614,0.00002822476,0.0005278821,0.0003254573],"genre_scores_gemma":[0.9613888,0.000001903912,0.01803509,0.0004635548,0.0002451902,0.01968558,0.00002536844,0.00003843885,0.0001161031],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9625188,"threshold_uncertainty_score":0.5899765,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04493907844073618,"score_gpt":0.3205472609028834,"score_spread":0.2756081824621472,"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."}}