{"id":"W4311358350","doi":"10.5539/cis.v16n1p39","title":"CNN Model for Sleep Apnea Detection Based on SpO2 Signal","year":2022,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Obstructive Sleep Apnea Research","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Softmax function; Computer science; Polysomnography; Convolutional neural network; Deep learning; Sleep apnea; Apnea; Artificial intelligence; SIGNAL (programming language); Pattern recognition (psychology); Pooling; Medicine; Speech recognition; Cardiology; Internal medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.0005879001,0.00005733925,0.0000718716,0.0003805269,0.000507312,0.00007715687,0.0001190052,0.00001307063,0.00003112985],"category_scores_gemma":[0.0000312952,0.00005131981,0.00002725805,0.00044222,0.0001262405,0.001160951,0.0001030359,0.00009082614,0.000008599474],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001174506,"about_ca_system_score_gemma":0.00005668445,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001206187,"about_ca_topic_score_gemma":7.548999e-8,"domain_scores_codex":[0.9989771,0.00001105729,0.0001455127,0.0001246796,0.0005712254,0.0001704413],"domain_scores_gemma":[0.9994892,0.00004402449,0.00004539238,0.0001263152,0.0001991436,0.00009594132],"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":[0.0002983966,0.00004506911,0.0002929323,0.00005202243,0.000003910197,4.81577e-7,0.0007748982,0.1643454,0.0021402,0.001381279,0.00006894641,0.8305964],"study_design_scores_gemma":[0.00086414,0.0005066266,0.002626054,0.000002070227,0.000003328515,0.00001012665,0.00005078767,0.9922002,0.002364083,0.00005791319,0.001258778,0.00005590948],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.069748,0.000002060817,0.9283946,0.0002511794,0.0001477208,0.0003644136,0.000009582724,0.0000296737,0.001052802],"genre_scores_gemma":[0.9904055,2.356851e-7,0.008109017,0.00137412,0.0000363517,0.000050992,0.00001129416,0.000002516462,0.000009975513],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9206575,"threshold_uncertainty_score":0.3901885,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02256806930249321,"score_gpt":0.2843645437115557,"score_spread":0.2617964744090625,"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."}}