{"id":"W2085866553","doi":"10.1109/ccece.2008.4564773","title":"Characterization of healthy and epileptic brain EEG signals by monofractal and multifractal analysis","year":2008,"lang":"en","type":"article","venue":"Conference proceedings - Canadian Conference on Electrical and Computer Engineering","topic":"Complex Systems and Time Series Analysis","field":"Economics, Econometrics and Finance","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Luonnontieteiden ja Tekniikan Tutkimuksen Toimikunta; University of Manitoba; Manitoba Health Research Council","keywords":"Electroencephalography; Multifractal system; Fractal dimension; Correlation dimension; Pattern recognition (psychology); Epileptic seizure; Fractal analysis; Fractal; Correlation; Brain activity and meditation; Artificial intelligence; Dimension (graph theory); Computer science; Neuroscience; Mathematics; Psychology; Mathematical analysis","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.0004500064,0.0003860833,0.0003028795,0.002812565,0.000202195,0.0004214441,0.0002144778,0.0003534892,0.0006453263],"category_scores_gemma":[0.001459725,0.00008507608,0.0004031893,0.0007680115,0.0003190226,0.0006369776,0.0001781656,0.0002441914,0.0001782585],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001626452,"about_ca_system_score_gemma":0.0001670139,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001255454,"about_ca_topic_score_gemma":0.001181081,"domain_scores_codex":[0.9997789,0.00004294958,0.00002098014,0.00004634776,0.00008901787,0.00002180353],"domain_scores_gemma":[0.9995093,0.0001961877,0.00009379943,0.00005552357,0.0001143735,0.00003081194],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0005944124,0.0001870056,0.02516843,0.0003371222,0.0001766527,0.0008718938,0.0005032788,0.02721796,0.2871943,0.00834616,0.001751879,0.6476508],"study_design_scores_gemma":[0.00004336928,0.0006485127,0.2324927,0.00006265584,0.0001700707,0.003298173,0.000502016,0.6707129,0.06879412,0.0128788,0.01022153,0.0001752221],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4441407,0.001964639,0.5497212,0.0002269851,0.00008068202,0.0001011643,0.0005602363,0.0004297351,0.002774709],"genre_scores_gemma":[0.8354483,0.0008287063,0.1620715,0.00004048282,0.0001380235,0.00004904297,0.000536231,0.00004198013,0.0008458259],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002812565,"threshold_uncertainty_score":0.002496302,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01660113293735499,"score_gpt":0.1793674898887484,"score_spread":0.1627663569513934,"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."}}