{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003784772,0.0005251765,0.0003026082,0.0006593981,0.0001045108,0.0002361367,0.0003917394,0.0002789121,0.0005019899],"category_scores_gemma":[0.0009069088,0.000207663,0.0002602098,0.0003608691,0.00009260551,0.0003388313,0.0003246865,0.0002726319,0.0001653405],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000522081,"about_ca_system_score_gemma":0.0004329293,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00587994,"about_ca_topic_score_gemma":0.009166582,"domain_scores_codex":[0.9998634,0.00002609331,0.00001042411,0.00003618844,0.00004015705,0.00002373864],"domain_scores_gemma":[0.9997527,0.00009508053,0.00004945557,0.00001735471,0.00007234435,0.00001308552],"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":[0.0004101855,0.0001187609,0.02455977,0.0001165658,0.0001184427,0.0004547004,0.00005539807,0.1925044,0.06016467,0.001225848,0.002698537,0.7175727],"study_design_scores_gemma":[0.000005641404,0.00006616237,0.007164319,0.00001495791,0.0000206733,0.000153624,0.0000119267,0.97208,0.01934334,0.0005877916,0.0005406589,0.0000108831],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4375895,0.001670898,0.5547583,0.0002348963,0.00006476614,0.00008037823,0.0005889782,0.002498236,0.002514021],"genre_scores_gemma":[0.9266267,0.0003658918,0.07095741,0.0000480915,0.0000157275,0.0000416908,0.0004869922,0.00003258229,0.001424935],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00587994,"threshold_uncertainty_score":0.01169145,"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."}}