{"id":"W2128267434","doi":"10.1504/ijbet.2012.049366","title":"Multistage preictal seizure analysis using Hidden Markov Model","year":2012,"lang":"en","type":"article","venue":"International Journal of Biomedical Engineering and Technology","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University Health Network","funders":"","keywords":"Hidden Markov model; Computer science; Pattern recognition (psychology); Epilepsy; Ictal; Electroencephalography; Artificial intelligence; Population; Speech recognition; Epileptic seizure; Sensitivity (control systems); Markov model; Gaussian; Markov chain; Machine learning; Neuroscience; Psychology; Medicine","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":[0.0001705685,0.0001019161,0.0001919551,0.001022464,0.00002484686,0.00003071956,0.0003739754,0.0001319967,0.00001756722],"category_scores_gemma":[0.000297514,0.00008323829,0.00007710385,0.0004064823,0.0001178171,0.0001773895,0.0001377161,0.0002578652,0.000001023032],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004453256,"about_ca_system_score_gemma":0.00002126176,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000195138,"about_ca_topic_score_gemma":1.726605e-7,"domain_scores_codex":[0.9990364,0.00001230553,0.000299482,0.0001184244,0.0003327373,0.0002006445],"domain_scores_gemma":[0.9995081,0.00008917809,0.0001332747,0.0000740496,0.0000741839,0.0001211616],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004742016,0.0003103735,0.002923538,0.00002347165,0.0008182675,0.000302477,0.0004469354,0.007443578,0.9233876,0.003155202,0.0002649374,0.06087621],"study_design_scores_gemma":[0.0004463336,0.00006688004,0.0002017856,0.00005014521,0.00009401155,0.001419876,0.0000381899,0.9530004,0.04231188,0.0001590614,0.00207082,0.0001405766],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8541071,0.0002101875,0.1441002,0.0008429374,0.0006295539,0.00002141197,0.00001402801,0.0000481522,0.00002642898],"genre_scores_gemma":[0.9787283,0.00003285927,0.02090069,0.00006771239,0.0002221094,6.83796e-7,0.000001004774,0.000008167464,0.00003844628],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9455569,"threshold_uncertainty_score":0.3394359,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01302077799587543,"score_gpt":0.2700441476633207,"score_spread":0.2570233696674453,"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."}}