{"id":"W2990357281","doi":"10.1109/access.2019.2955738","title":"Inter-Patient CNN-LSTM for QRS Complex Detection in Noisy ECG Signals","year":2019,"lang":"en","type":"article","venue":"IEEE Access","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; Nvidia","keywords":"Computer science; QRS complex; Artificial intelligence; Pattern recognition (psychology); Speech recognition; Cardiology; Medicine","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":[0.000127574,0.00010364,0.0002629591,0.0002013008,0.00003292775,0.00003944739,0.0001196737,0.00006212084,0.0001030615],"category_scores_gemma":[0.00003996071,0.00009024318,0.0001097405,0.0002457169,0.00001400634,0.0001316052,0.00002608598,0.0001141804,0.00006616109],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000845986,"about_ca_system_score_gemma":0.00001831425,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003708261,"about_ca_topic_score_gemma":0.0001103068,"domain_scores_codex":[0.9991666,0.00002363885,0.0002454435,0.0002286668,0.0001428571,0.0001927982],"domain_scores_gemma":[0.9994636,0.00008024824,0.00008280937,0.0002204629,0.00009067418,0.00006218522],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005239611,0.0004284556,0.387515,0.0004538057,0.0002143472,0.00002057994,0.0005906674,0.001509289,0.349952,0.000003696612,0.001675404,0.2571128],"study_design_scores_gemma":[0.004434665,0.001410079,0.08700124,0.0006709009,0.0002804327,0.00001475753,0.0006071266,0.09242538,0.8007368,0.0003170967,0.01155413,0.0005474217],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9917955,0.00003000841,0.006265678,0.0001849416,0.0006685129,0.000372288,0.000004262876,0.00004201196,0.0006367795],"genre_scores_gemma":[0.9986291,0.000006667703,0.0002508333,0.0002323138,0.0002456714,0.00005124215,0.00001048098,0.00001739439,0.0005563142],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4507847,"threshold_uncertainty_score":0.368001,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05441126345129077,"score_gpt":0.3591861367216098,"score_spread":0.304774873270319,"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."}}