{"id":"W2936978234","doi":"10.1109/access.2019.2907076","title":"Real-Time Detection of Acute Cognitive Stress Using a Convolutional Neural Network From Electrocardiographic Signal","year":2019,"lang":"en","type":"article","venue":"IEEE Access","topic":"Heart Rate Variability and Autonomic Control","field":"Medicine","cited_by":104,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Convolutional neural network; Computer science; SIGNAL (programming language); Cognition; Artificial intelligence; Speech recognition; Pattern recognition (psychology); Neuroscience; Psychology","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0001931135,0.0001443417,0.000442262,0.0001011105,0.00006315481,0.00002927666,0.00009695159,0.0001240819,0.0002946072],"category_scores_gemma":[0.00001153764,0.000138053,0.000193147,0.0003305273,0.00007549692,0.0002331807,0.00003003643,0.0002134148,0.00002525404],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006125343,"about_ca_system_score_gemma":0.0001853344,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001061296,"about_ca_topic_score_gemma":0.0000170718,"domain_scores_codex":[0.99877,0.0001212377,0.0003144545,0.0003048277,0.0001997386,0.0002897341],"domain_scores_gemma":[0.9991462,0.0002783897,0.000135289,0.0001765239,0.0001743012,0.00008929249],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0005485438,0.00007738855,0.07019545,0.00002754644,0.0007352986,0.00001032435,0.00004208403,0.00198668,0.9248012,0.000006795866,0.00001206892,0.001556633],"study_design_scores_gemma":[0.0048661,0.0005912045,0.4318103,0.0003878467,0.001568425,0.00003925303,0.00003274495,0.337988,0.2217559,0.0005174018,0.00006232833,0.0003804736],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9960027,0.0000640537,0.002672772,0.00004322381,0.0003009075,0.0005427407,0.0001288881,0.00004933638,0.000195346],"genre_scores_gemma":[0.9991721,0.00001858184,0.00007903339,0.0001000591,0.0005084397,0.00001221487,0.0000606781,0.00001689028,0.00003200996],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7030453,"threshold_uncertainty_score":0.5629637,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01553669001268003,"score_gpt":0.2783784979523465,"score_spread":0.2628418079396664,"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."}}