{"id":"W4415423296","doi":"10.1016/j.eswa.2025.130091","title":"Consciousness-ECG transformer for conscious state estimation system with real-time monitoring","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministry of Science and ICT, South Korea; Iran Telecommunication Research Center; National Research Foundation of Korea; Information Technology Research Centre; Institute for Information and Communications Technology Promotion; Korea University","keywords":"Transformer; Electroencephalography; Heart rate variability; Sleep (system call); Noise (video); Consciousness; Electrocardiography; State (computer science)","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":[0.0001601644,0.000255415,0.0003380881,0.0001596028,0.0005205828,0.0002234814,0.0003321521,0.00006678492,0.000001785482],"category_scores_gemma":[0.0000131208,0.0001862913,0.00004558451,0.0004516535,0.0001588088,0.0002110833,0.00001442658,0.00009087571,0.0000385259],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001446496,"about_ca_system_score_gemma":0.0001545035,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002094202,"about_ca_topic_score_gemma":0.00001207197,"domain_scores_codex":[0.9983124,0.00006869181,0.000411199,0.0006042767,0.000252923,0.000350467],"domain_scores_gemma":[0.9985918,0.0004862141,0.0001754264,0.0004786623,0.0001689964,0.00009889749],"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":[0.0007696796,0.0005201008,0.001591091,0.002732242,0.0002893425,0.00002173824,0.006941947,0.03228363,0.8728736,0.060476,0.006266316,0.01523429],"study_design_scores_gemma":[0.004610203,0.0006720333,0.0001991571,0.003296519,0.0001247663,0.000366331,0.00423796,0.2662445,0.6789124,0.0002197044,0.03973006,0.001386316],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03425755,0.0002013198,0.9552184,0.0003991887,0.0002920861,0.004230651,0.00009322076,0.0006855051,0.004622087],"genre_scores_gemma":[0.9840835,0.0000180085,0.006382026,0.00005817907,0.00008648501,0.007001558,0.00001151356,0.00003823169,0.002320523],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9498259,"threshold_uncertainty_score":0.759674,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01465034622665973,"score_gpt":0.2836405009802866,"score_spread":0.2689901547536269,"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."}}