{"id":"W2889853479","doi":"10.1109/icassp.2018.8461999","title":"Ecg Delineation for Qt Interval Analysis Using an Unsupervised Learning Method","year":2018,"lang":"en","type":"article","venue":"","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Inflection point; Artificial intelligence; Pattern recognition (psychology); Cluster analysis; Computer science; Unsupervised learning; Noise (video); Gaussian filter; Expectation–maximization algorithm; Energy (signal processing); Filter (signal processing); Algorithm; Mathematics; Computer vision; Statistics","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":[0.0007443428,0.0004973396,0.0006750527,0.001467287,0.0004357429,0.0005842129,0.001002597,0.0007263546,0.001482164],"category_scores_gemma":[0.002534075,0.0002337343,0.0006448762,0.001012904,0.0004063367,0.0006989484,0.0006201034,0.0007600631,0.001154009],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003096304,"about_ca_system_score_gemma":0.0006115201,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001348997,"about_ca_topic_score_gemma":0.001609892,"domain_scores_codex":[0.9991621,0.0001499794,0.00007156845,0.0002953324,0.0002695623,0.00005145982],"domain_scores_gemma":[0.9989465,0.0004170493,0.000178499,0.0001543103,0.0002750855,0.00002856619],"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":[0.0001791832,0.0001370459,0.002870281,0.0001540164,0.00009268524,0.0001538839,0.0001711433,0.06311731,0.05006156,0.004856913,0.002709946,0.875496],"study_design_scores_gemma":[0.00001789333,0.00007406781,0.003334936,0.0000202161,0.00003078057,0.0004096573,0.00003186061,0.969092,0.01967981,0.003572841,0.003700664,0.00003538803],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006173184,0.00008626175,0.9925006,0.00002362306,0.0000107977,0.00003330496,0.0000402056,0.000721583,0.0004104763],"genre_scores_gemma":[0.1899432,0.0001655412,0.8075981,0.00006971982,0.00007248898,0.0001460413,0.0003215748,0.0002167883,0.001466676],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001482164,"threshold_uncertainty_score":0.004958332,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09476910725860276,"score_gpt":0.4369135295678211,"score_spread":0.3421444223092183,"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."}}