{"id":"W2528297920","doi":"10.1016/j.clinph.2016.09.011","title":"Differentiating epileptic from non-epileptic high frequency intracerebral EEG signals with measures of wavelet entropy","year":2016,"lang":"en","type":"article","venue":"Clinical Neurophysiology","topic":"Epilepsy research and treatment","field":"Medicine","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"Montreal Neurological Institute and Hospital; Queen's University; McGill University","funders":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Canadian Institutes of Health Research; Austrian Science Fund","keywords":"Standard deviation; Epilepsy; Epileptic seizure; Logistic regression; Electroencephalography; Mathematics; Statistics; Ripple; Wavelet; Pattern recognition (psychology); Audiology; Cardiology; Medicine; Psychology; Artificial intelligence; Physics; Computer science; Neuroscience","routes":{"ca_aff":true,"ca_fund":true,"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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001770875,0.0003170449,0.001187413,0.00009277056,0.00006813339,0.000009138394,0.0002493157,0.0002066645,0.001172584],"category_scores_gemma":[0.001343469,0.000166346,0.000297374,0.0001491765,0.000777408,0.00007082037,0.0001253345,0.0004639876,0.0003116315],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004079836,"about_ca_system_score_gemma":0.0001795555,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001315646,"about_ca_topic_score_gemma":0.00001365496,"domain_scores_codex":[0.9966556,0.0005944996,0.0009832967,0.0007626255,0.0003902192,0.0006137388],"domain_scores_gemma":[0.9956266,0.002599075,0.0003363727,0.0007522759,0.0002459633,0.0004397484],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.002202236,0.001349265,0.2839793,0.00005649349,0.0006205304,0.0003672728,0.00002525651,0.000002235077,0.6873647,0.0002582717,0.0001851027,0.02358936],"study_design_scores_gemma":[0.00634309,0.01003149,0.9700714,0.0002751948,0.0002540489,0.00001149237,0.00001115529,0.0000822389,0.01007687,0.002630842,0.00002281722,0.0001893712],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9969147,0.00004760383,0.0007109234,0.001288376,0.0002678109,0.0005125281,0.00007637203,0.0000541321,0.0001275217],"genre_scores_gemma":[0.9960826,0.0003518098,0.002341188,0.0004686958,0.000484259,0.00004255464,0.00005457034,0.00005065896,0.0001236652],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6860921,"threshold_uncertainty_score":0.9997405,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.043420323516389,"score_gpt":0.3204496609774792,"score_spread":0.2770293374610903,"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."}}