{"id":"W3130906033","doi":"10.1007/s10439-021-02732-z","title":"Detection of Apnea Bradycardia from ECG Signals of Preterm Infants Using Layered Hidden Markov Model","year":2021,"lang":"en","type":"article","venue":"Annals of Biomedical Engineering","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Rehabilitation Institute; University of Toronto; University Health Network","funders":"","keywords":"Hidden Markov model; Bradycardia; Apnea; QRS complex; Electrocardiography; Medicine; Pattern recognition (psychology); Artificial intelligence; Computer science; Cardiology; Internal medicine; Heart rate","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002436221,0.0003392282,0.0003350037,0.0003543504,0.00009041194,0.000218623,0.0002694635,0.0003618432,0.0002643896],"category_scores_gemma":[0.0009351919,0.0001471311,0.0003484197,0.0001593068,0.00005834371,0.0002035847,0.0002406374,0.000408771,0.0001493076],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000122869,"about_ca_system_score_gemma":0.0002530236,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001965739,"about_ca_topic_score_gemma":0.002395343,"domain_scores_codex":[0.999904,0.00002185623,0.000007402316,0.00002266848,0.00002542702,0.00001866831],"domain_scores_gemma":[0.9996983,0.0001913894,0.00003326654,0.0000111782,0.00004746784,0.0000184788],"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":[0.001734006,0.0005206955,0.1066353,0.000283968,0.0003008946,0.001206167,0.0002455774,0.1392458,0.2527873,0.001036395,0.002035614,0.4939682],"study_design_scores_gemma":[0.000009535458,0.0001489501,0.0238353,0.00001138082,0.00004048292,0.0002112988,0.00002418284,0.9636124,0.01161012,0.0003385411,0.0001433924,0.00001441509],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6974612,0.0009151417,0.2998437,0.0001783451,0.0000495144,0.00002723761,0.0003710864,0.0005708705,0.0005828587],"genre_scores_gemma":[0.9763665,0.0002508082,0.02242294,0.00002873456,0.00001828732,0.00001563031,0.0004147913,0.00001085902,0.0004714508],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001965739,"threshold_uncertainty_score":0.003908634,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02655141916306628,"score_gpt":0.2544031165526209,"score_spread":0.2278516973895547,"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."}}