{"id":"W3183373935","doi":"10.1109/access.2021.3097090","title":"Sleep Apnea Detection From Variational Mode Decomposed EEG Signal Using a Hybrid CNN-BiLSTM","year":2021,"lang":"en","type":"article","venue":"IEEE Access","topic":"Obstructive Sleep Apnea Research","field":"Medicine","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Pattern recognition (psychology); Feature extraction; Electroencephalography; Artificial intelligence; Sleep apnea; Convolutional neural network; Kernel (algebra); Sleep Stages; Artificial neural network; Deep learning; Feature (linguistics); Speech recognition; Polysomnography; Medicine; Mathematics","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001670953,0.0002023906,0.0003286903,0.000193058,0.0001942908,0.0001832097,0.000249769,0.0001070047,0.001642836],"category_scores_gemma":[0.0001567795,0.0002083434,0.0001411577,0.0004702398,0.00007196525,0.000495694,0.0001407325,0.0004097728,0.00006400167],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003555903,"about_ca_system_score_gemma":0.0001200494,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006899298,"about_ca_topic_score_gemma":0.00006445139,"domain_scores_codex":[0.9977363,0.000162353,0.0003494339,0.0005699937,0.0007811781,0.0004007339],"domain_scores_gemma":[0.9984459,0.0002570482,0.0001132593,0.0004094013,0.0005498515,0.0002245157],"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":[0.0004037877,0.000274095,0.01265639,0.00004580931,0.0003488905,0.0003967565,0.00009788689,0.00246752,0.9385614,0.00001864149,0.00002969633,0.04469915],"study_design_scores_gemma":[0.001562088,0.00003784307,0.016388,0.0000159148,0.0001078983,0.00009442074,0.00002873506,0.5251283,0.4555128,0.0008524093,0.0001154143,0.0001561808],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7016972,0.00006543408,0.2965458,0.0001569743,0.0005707716,0.0002309425,0.00006986256,0.00006246304,0.0006006249],"genre_scores_gemma":[0.9938592,0.000002409322,0.004861885,0.000237835,0.0008083905,0.0000256005,0.0001272133,0.000047269,0.00003017588],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5226608,"threshold_uncertainty_score":0.9992698,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04345987263077773,"score_gpt":0.3526929335971843,"score_spread":0.3092330609664066,"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."}}