{"id":"W2060529818","doi":"10.1016/j.medengphy.2012.05.005","title":"Automatic seizure detection in SEEG using high frequency activities in wavelet domain","year":2012,"lang":"en","type":"article","venue":"Medical Engineering & Physics","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":76,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"Canadian Institutes of Health Research","keywords":"Electroencephalography; Ictal; Stereoelectroencephalography; Thresholding; Artifact (error); Epilepsy; Pattern recognition (psychology); Wavelet; Sensitivity (control systems); Frequency domain; Artificial intelligence; Computer science; Psychology; Neuroscience; Computer vision; Electronic engineering; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005473014,0.000398697,0.0003951834,0.0006986607,0.0001131028,0.0004602883,0.0002459717,0.0004854799,0.001440021],"category_scores_gemma":[0.001652788,0.0001404421,0.0002074079,0.0004614743,0.0002021688,0.0004167274,0.0002468231,0.0002101799,0.0009089239],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009710429,"about_ca_system_score_gemma":0.0001150171,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000281704,"about_ca_topic_score_gemma":0.0005806725,"domain_scores_codex":[0.9997026,0.0001014328,0.00001636602,0.0000605069,0.00008959412,0.00002948707],"domain_scores_gemma":[0.9995444,0.0002765004,0.00005150201,0.0000336484,0.00007535335,0.00001863883],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008335952,0.0001600115,0.01356184,0.0004068768,0.00007264622,0.0003223034,0.0001538085,0.003325227,0.3410335,0.0006993501,0.001208154,0.6382227],"study_design_scores_gemma":[0.0003021805,0.002478404,0.2408484,0.0002082466,0.0003172102,0.006045803,0.0002279591,0.3607199,0.3759607,0.003929244,0.008815981,0.0001460349],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4188439,0.0009139081,0.5727659,0.0001536377,0.00008124836,0.0002589041,0.0004701996,0.003085634,0.003426726],"genre_scores_gemma":[0.7712624,0.0006328688,0.2253774,0.000114594,0.00005232804,0.0001951983,0.0005168313,0.0001222682,0.001726004],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001440021,"threshold_uncertainty_score":0.004817367,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0143885759111559,"score_gpt":0.2405810722580022,"score_spread":0.2261924963468463,"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."}}