{"id":"W4405847588","doi":"10.1016/j.jelectrocard.2024.153858","title":"MrSeNet: Electrocardiogram signal denoising based on multi-resolution residual attention network","year":2024,"lang":"en","type":"article","venue":"Journal of Electrocardiology","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"PROTO Manufacturing (Canada)","funders":"","keywords":"Residual; Noise reduction; SIGNAL (programming language); Artificial intelligence; Computer science; Pattern recognition (psychology); Speech recognition; Algorithm","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":[0.001201343,0.0002106317,0.000685104,0.0005645021,0.0001265981,0.0000483812,0.00009938473,0.0002163602,0.00001280876],"category_scores_gemma":[0.0001066568,0.0001679407,0.0008151863,0.0006472278,0.00005324082,0.00007655148,0.00001159685,0.0009080579,0.00002129879],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002802552,"about_ca_system_score_gemma":0.0003727294,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008138206,"about_ca_topic_score_gemma":0.000003087825,"domain_scores_codex":[0.997879,0.000326523,0.0005647815,0.0002750131,0.0004082069,0.0005464516],"domain_scores_gemma":[0.9990327,0.0002163926,0.0001879864,0.0001848346,0.0002272604,0.0001507991],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00528285,0.0005858016,0.154414,0.0003397609,0.009994301,0.005017089,0.00009986552,0.3211174,0.3773674,0.000601134,0.02768329,0.09749715],"study_design_scores_gemma":[0.009640224,0.03206458,0.2855415,0.004198676,0.01213676,0.01491573,0.0001605073,0.5780869,0.02212546,0.001565855,0.03802233,0.001541433],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4056037,0.01952984,0.569463,0.002466585,0.001537957,0.0002417089,0.00000186566,0.0001833042,0.0009720565],"genre_scores_gemma":[0.9882442,0.0003508049,0.005252733,0.0002350577,0.005669936,0.000003373744,0.00001442395,0.00003789691,0.0001916267],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5826405,"threshold_uncertainty_score":0.6848424,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01586218314157469,"score_gpt":0.2938387879955881,"score_spread":0.2779766048540134,"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."}}