{"id":"W3110157010","doi":"10.1109/iccis49240.2020.9257700","title":"A Novel Approach to Classify Electrocardiogram Signals Using Deep Neural Networks","year":2020,"lang":"en","type":"article","venue":"","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Artificial intelligence; Process (computing); Convolutional neural network; Task (project management); Atrial fibrillation; Pattern recognition (psychology); Normal Sinus Rhythm; Rhythm; Artificial neural network; Sinus rhythm; Machine learning; Speech recognition; Engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.00009433092,0.0001469174,0.0003632285,0.00007630211,0.0000689305,0.00004090367,0.00008506254,0.00008164022,0.00001741186],"category_scores_gemma":[0.00005005697,0.0001181609,0.0002339241,0.0008334547,0.0000153278,0.00003702447,0.00004151498,0.0002488693,0.000009497757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003472252,"about_ca_system_score_gemma":0.00001743091,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006368126,"about_ca_topic_score_gemma":9.738383e-7,"domain_scores_codex":[0.9988948,0.00002124263,0.0001971187,0.0003212616,0.0002238,0.000341838],"domain_scores_gemma":[0.9993094,0.00002418675,0.00003242813,0.0001713115,0.0000645792,0.0003980927],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003921872,0.0005564741,0.04401751,0.0001326874,0.001411014,0.00005232796,0.0006613968,0.634384,0.2574304,0.0001288527,0.002206075,0.05862713],"study_design_scores_gemma":[0.000408747,0.0001716692,0.0007251039,0.000009952118,0.000296186,0.00002809556,0.0001903937,0.9966914,0.0009586958,0.000001402458,0.0003809351,0.0001373839],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05421529,0.0001874104,0.938887,0.001325671,0.00005170373,0.0001723151,4.285559e-7,0.0001717267,0.00498845],"genre_scores_gemma":[0.9520637,0.000004349057,0.04330954,0.003164562,0.001272224,0.000008576572,0.000008489545,0.00002534782,0.0001432169],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8978484,"threshold_uncertainty_score":0.4818462,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06176295701635784,"score_gpt":0.29254582760339,"score_spread":0.2307828705870321,"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."}}