{"id":"W2000363394","doi":"10.1109/iembs.2011.6090381","title":"Capturing the state transitions of seizure-like events using Hidden Markov models","year":2011,"lang":"en","type":"article","venue":"","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; Toronto Western Hospital; University of Toronto","funders":"","keywords":"Computer science; Hidden Markov model; Markov chain; Markov process; State (computer science); Markov model; Artificial intelligence; Machine learning; Algorithm; Mathematics; Statistics","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":[0.0007545656,0.0005368735,0.0004813911,0.0005533875,0.0001977472,0.0005380615,0.0004703413,0.0005425295,0.0007463888],"category_scores_gemma":[0.003133029,0.0004099321,0.0006198171,0.0003222328,0.0002586364,0.000768521,0.0003444412,0.0008085796,0.000245116],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004720422,"about_ca_system_score_gemma":0.0005595456,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008972615,"about_ca_topic_score_gemma":0.01087059,"domain_scores_codex":[0.9997892,0.00006317002,0.00001564946,0.00005814064,0.00003597398,0.00003789917],"domain_scores_gemma":[0.997823,0.001734266,0.0002025506,0.00009569575,0.0001016221,0.00004284712],"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":[0.0004488929,0.0001401029,0.02235812,0.0001177915,0.0001150774,0.0003253545,0.0002684401,0.8797278,0.01550399,0.004531293,0.0006282644,0.0758348],"study_design_scores_gemma":[0.000003573886,0.00001671368,0.001482003,0.000004460682,0.000008268537,0.00001734025,0.00001095481,0.9953243,0.001361197,0.001673829,0.00009001304,0.00000739546],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3284913,0.0002655107,0.6687621,0.0001617356,0.00002481351,0.00004131341,0.0004356264,0.001056938,0.0007606272],"genre_scores_gemma":[0.9528995,0.0001504265,0.04556072,0.00002395009,0.00001082184,0.00004035484,0.0005986587,0.00004081714,0.0006747264],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008972615,"threshold_uncertainty_score":0.0178408,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09098632505888331,"score_gpt":0.2690499030356652,"score_spread":0.1780635779767819,"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."}}