{"id":"W4293652293","doi":"10.1016/j.bspc.2022.104053","title":"Seizure detection algorithm based on improved functional brain network structure feature extraction","year":2022,"lang":"en","type":"article","venue":"Biomedical Signal Processing and Control","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":68,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Natural Science Foundation of Zhejiang Province; Wuhan University School of Medicine; National Natural Science Foundation of China; BC Children's Hospital","keywords":"Computer science; Support vector machine; Pattern recognition (psychology); Electroencephalography; Artificial intelligence; Principal component analysis; Feature extraction; Epilepsy; Feature (linguistics); Correlation; Epileptic seizure; Correlation coefficient; Frequency domain; Machine learning; Mathematics; Psychology; Neuroscience","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.0002868378,0.0001982884,0.0001831942,0.00009983857,0.001042768,0.0001570338,0.0001438227,0.0001328837,0.0002281556],"category_scores_gemma":[0.00006686959,0.0001594223,0.0000615242,0.0003768664,0.0001332511,0.0001226574,0.00003961811,0.0007351644,0.000001606418],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006653409,"about_ca_system_score_gemma":0.0001025585,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005622348,"about_ca_topic_score_gemma":0.000001142744,"domain_scores_codex":[0.9980967,0.0002528105,0.0001861856,0.0005434505,0.0005849515,0.0003358729],"domain_scores_gemma":[0.9992008,0.0003807258,0.0001400176,0.00008890296,0.0000327089,0.0001568715],"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":[0.0004016046,0.00008993412,0.00001040794,0.00002162826,0.00000654312,0.00001942438,0.00003034069,0.001648565,0.38641,0.000007799441,0.002053533,0.6093002],"study_design_scores_gemma":[0.001985757,0.0007813053,0.0003486618,0.00003267203,0.00002115375,0.0001501467,0.00003981895,0.9496033,0.009571861,0.0005530518,0.0366841,0.0002282457],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02967362,0.0004625471,0.9559444,0.01130644,0.001513727,0.0004643833,0.0001667934,0.0003472393,0.0001208206],"genre_scores_gemma":[0.989837,5.797848e-7,0.00039773,0.008327521,0.001094819,0.00004339938,0.0000201926,0.00001787229,0.0002608717],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9601634,"threshold_uncertainty_score":0.8020232,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008474239361660153,"score_gpt":0.2310289606327532,"score_spread":0.222554721271093,"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."}}