{"id":"W3090787545","doi":"10.3389/fphys.2020.569050","title":"Deep Learning Algorithm Classifies Heartbeat Events Based on Electrocardiogram Signals","year":2020,"lang":"en","type":"article","venue":"Frontiers in Physiology","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Guangxi Key Research and Development Program; National Natural Science Foundation of China","keywords":"Heartbeat; Artificial intelligence; Computer science; Deep learning; Convolutional neural network; Artificial neural network; Machine learning; Event (particle physics); Pattern recognition (psychology); Data mining","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":[0.0005170997,0.0007599645,0.0006329918,0.000795612,0.0002137027,0.0005583189,0.0007358102,0.0006262136,0.001432205],"category_scores_gemma":[0.001465915,0.0001875442,0.0004131247,0.0004211381,0.0001723062,0.000581439,0.0004864439,0.0007881334,0.0006094699],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004349606,"about_ca_system_score_gemma":0.0006139793,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00632798,"about_ca_topic_score_gemma":0.005904701,"domain_scores_codex":[0.9997699,0.00002075946,0.0000228407,0.00007456417,0.00006770685,0.00004428234],"domain_scores_gemma":[0.9997469,0.00007452109,0.00003197577,0.00002233691,0.0001070139,0.00001732924],"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":[0.0002725162,0.0004503642,0.01442393,0.00009511365,0.0001192378,0.0001620583,0.00006817735,0.1134448,0.02399112,0.001353098,0.004391661,0.8412279],"study_design_scores_gemma":[0.00001226439,0.00009283283,0.003530303,0.00001484541,0.00001926457,0.00004961511,0.00001719695,0.9887728,0.006114738,0.0005781257,0.0007889316,0.000009115426],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2853197,0.001157136,0.7025753,0.000567875,0.000282763,0.0002836451,0.0006286289,0.002971039,0.006213852],"genre_scores_gemma":[0.8804784,0.0005909115,0.1107827,0.0002638666,0.00006420141,0.0001585437,0.00114374,0.00005966671,0.00645794],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00632798,"threshold_uncertainty_score":0.0125823,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01263392139885665,"score_gpt":0.2538348389535124,"score_spread":0.2412009175546557,"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."}}