{"id":"W4224981468","doi":"10.1109/tbme.2022.3170047","title":"Signal Quality Assessment of Compressively Sensed Electrocardiogram","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Compute Canada","keywords":"Mathematics; Signal-to-noise ratio (imaging); Artifact (error); Noise (video); Mean squared error; Statistics; Pattern recognition (psychology); Artificial intelligence; Computer science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002048488,0.0008055733,0.000412416,0.00100427,0.0001509508,0.0006366664,0.0003484723,0.0005875833,0.0008101264],"category_scores_gemma":[0.009297724,0.0001293388,0.0002770118,0.0004141404,0.0004425639,0.0007748536,0.0005590168,0.0003597577,0.0002102709],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002424191,"about_ca_system_score_gemma":0.0001999685,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008927326,"about_ca_topic_score_gemma":0.0007293925,"domain_scores_codex":[0.9989308,0.000179912,0.00009710299,0.0001802211,0.0005624451,0.00004956992],"domain_scores_gemma":[0.996532,0.001391035,0.0005179626,0.0002310364,0.001199527,0.0001285063],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.004237204,0.0005015617,0.08196682,0.0009977001,0.0004764196,0.0008018994,0.0003714144,0.1613935,0.2794023,0.001199716,0.002197812,0.4664536],"study_design_scores_gemma":[0.0001398402,0.003169989,0.1282107,0.0001202236,0.0002318362,0.002147709,0.0002065422,0.7386267,0.124024,0.001350953,0.001655808,0.0001157853],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7253723,0.001287634,0.2698008,0.000263802,0.0001046742,0.0001568647,0.0006233772,0.0006114299,0.001779049],"genre_scores_gemma":[0.9526113,0.0002986528,0.04568515,0.00008104151,0.00007463792,0.00004290022,0.000764366,0.00003455002,0.0004074842],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002048488,"threshold_uncertainty_score":0.01083362,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02082695397855388,"score_gpt":0.3039301235481004,"score_spread":0.2831031695695465,"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."}}