{"id":"W2914534442","doi":"10.1109/tim.2018.2855518","title":"Accurate Classification of Seizure and Seizure-Free Intervals of Intracranial EEG Signals From Epileptic Patients","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":96,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Electroencephalography; Pattern recognition (psychology); Computer science; Epileptic seizure; Artificial intelligence; CAD; Classifier (UML); Hurst exponent; Machine learning; Speech recognition; Mathematics; Statistics; Engineering; Neuroscience","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":[0.0006517957,0.0004258907,0.0005159997,0.001663425,0.0001327129,0.0005946018,0.0002961612,0.0004761527,0.0004881679],"category_scores_gemma":[0.004164844,0.00009499974,0.000250874,0.0004702626,0.0001228852,0.0004617694,0.0003071724,0.0003079048,0.0004229983],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001546104,"about_ca_system_score_gemma":0.0002059815,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001199789,"about_ca_topic_score_gemma":0.001673262,"domain_scores_codex":[0.9995281,0.00009120806,0.00009114736,0.000112363,0.0001356711,0.00004148002],"domain_scores_gemma":[0.998454,0.0006094861,0.0002435199,0.0001939728,0.0004203022,0.00007872924],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001623582,0.00040341,0.2567154,0.0002099442,0.0001728313,0.0009039794,0.000258353,0.0267451,0.05593771,0.0005277005,0.006318409,0.6501835],"study_design_scores_gemma":[0.00007127581,0.0004007237,0.3672171,0.00004467257,0.0001209377,0.002134404,0.0002144453,0.5942077,0.03224746,0.0007792119,0.002481853,0.00008029406],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.947253,0.0007230592,0.04843009,0.0001397737,0.00009687586,0.00006084752,0.0009803552,0.001074904,0.001241136],"genre_scores_gemma":[0.9862254,0.0001317732,0.01191906,0.00002881515,0.00003927481,0.00001986461,0.001235445,0.00002302029,0.0003774328],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001663425,"threshold_uncertainty_score":0.003447056,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06347261979466605,"score_gpt":0.2849385648771873,"score_spread":0.2214659450825212,"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."}}