{"id":"W2795691199","doi":"10.1142/s0129065718500119","title":"Neonatal Seizure Detection Using Deep Convolutional Neural Networks","year":2018,"lang":"en","type":"article","venue":"International Journal of Neural Systems","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":232,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Artificial intelligence; Convolutional neural network; Pattern recognition (psychology); Classifier (UML); Random forest; Epileptic seizure; Electroencephalography; Feature selection; Constant false alarm rate; Deep learning; Neonatal seizure; Word error rate; Machine learning; Speech recognition","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":[0.0002589036,0.0001894033,0.0002333946,0.0002442921,0.0001638511,0.0003057673,0.00076578,0.00009338575,0.00004586534],"category_scores_gemma":[0.0001789253,0.0001559023,0.000179673,0.0001775191,0.0001843194,0.0007286133,0.0001207756,0.0003757978,0.00001416413],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001761565,"about_ca_system_score_gemma":0.00003597433,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004808245,"about_ca_topic_score_gemma":0.0000133826,"domain_scores_codex":[0.9976864,0.0002281698,0.0006948587,0.0002564725,0.0008602109,0.0002738863],"domain_scores_gemma":[0.9981921,0.0002312719,0.000665504,0.0001149849,0.0006646526,0.0001314864],"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":[0.001400592,0.000219283,0.005133395,0.00003168063,0.0002925016,0.001587239,0.0009745524,0.3521069,0.5868995,0.001002588,0.00111068,0.04924111],"study_design_scores_gemma":[0.0006178769,0.0003101752,0.0007740335,0.00007434036,0.000014131,0.01291365,0.00007586214,0.9633741,0.01916071,0.00005177879,0.002469118,0.0001642566],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9182506,0.0002743319,0.06218279,0.0003217141,0.01869513,0.0001078371,0.00001128523,0.00003687394,0.0001194035],"genre_scores_gemma":[0.9929155,0.000006855652,0.0001146681,0.0004637589,0.006394943,0.000001507394,0.000001278484,0.00001972216,0.00008179768],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6112671,"threshold_uncertainty_score":0.635751,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03608114269235557,"score_gpt":0.2953572967930639,"score_spread":0.2592761541007083,"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."}}