{"id":"W3162952110","doi":"10.18280/ts.380210","title":"Seizure Detection Based on Adaptive Feature Extraction by Applying Extreme Learning Machines","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Extreme learning machine; Pattern recognition (psychology); Artificial intelligence; Preprocessor; Computer science; Electroencephalography; Ictal; Feature extraction; Parseval's theorem; Signal processing; Epileptic seizure; Speech recognition; Mathematics; Artificial neural network; Radar; Short-time Fourier transform; Psychology; Fourier transform; Neuroscience","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001711175,0.0002186034,0.0001535013,0.00008817442,0.0003487102,0.0001448791,0.0001315041,0.00008988254,0.0003857618],"category_scores_gemma":[0.00006473172,0.0002005813,0.00009623278,0.0002559418,0.00003338539,0.0002204484,0.00002896419,0.0004912505,0.00003177156],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000749781,"about_ca_system_score_gemma":0.00002855807,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008985073,"about_ca_topic_score_gemma":0.000009694591,"domain_scores_codex":[0.9982697,0.0002890858,0.000183602,0.0005523451,0.0004330075,0.0002722677],"domain_scores_gemma":[0.9993386,0.0002959982,0.0001193067,0.0001223402,0.00004902753,0.00007473882],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000137144,0.0001599069,0.0001433747,0.00001688669,0.000008064922,0.00005377867,0.0001556923,0.01287605,0.9246268,0.00004229882,0.001138882,0.06064112],"study_design_scores_gemma":[0.0005627952,0.0002871168,0.0004580642,0.00006492691,0.00001416641,0.00003685213,0.000110516,0.2747195,0.7083575,0.00004015572,0.01513927,0.0002091518],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6337005,0.000179227,0.3581569,0.001689899,0.0009230564,0.0006617123,0.0000440795,0.0005379768,0.004106639],"genre_scores_gemma":[0.9970523,0.000005340566,0.0004972107,0.00108981,0.0002375248,0.00005289104,0.00001737375,0.00002436235,0.00102316],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3633519,"threshold_uncertainty_score":0.8179467,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03255329160028756,"score_gpt":0.2584865941663499,"score_spread":0.2259333025660624,"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."}}