{"id":"W2066161270","doi":"10.1109/bhi.2014.6864460","title":"Time Frequency noise canceller for an optimized separation of the ECG from low back sEMG signals","year":2014,"lang":"en","type":"article","venue":"IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...)","topic":"Advanced Adaptive Filtering Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal; Université du Québec à Trois-Rivières","funders":"U.S. Bureau of Land Management","keywords":"Computer science; Noise (video); Active noise control; Adaptive filter; Speech recognition; Noise measurement; Electronic engineering; Artificial intelligence; Noise reduction; Algorithm; 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":[0.0003384077,0.0001724513,0.0002764863,0.00009329395,0.00007200579,0.00003510405,0.0003444699,0.0001124862,0.0002053088],"category_scores_gemma":[0.0000444393,0.00013023,0.00004846989,0.00007239964,0.0001540608,0.0002462116,0.00003566919,0.0001791282,0.00002375888],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001006993,"about_ca_system_score_gemma":0.00009344882,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006129152,"about_ca_topic_score_gemma":0.00001307235,"domain_scores_codex":[0.9985984,0.00003008415,0.0007087517,0.0001189814,0.0003347427,0.0002090266],"domain_scores_gemma":[0.9991225,0.0001010597,0.0002458877,0.0002053892,0.0001644742,0.0001607083],"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":[0.001361708,0.00151319,0.0005638069,0.006085957,0.001105246,0.000002273388,0.02267448,0.09938325,0.1363503,0.1728248,0.1186297,0.4395053],"study_design_scores_gemma":[0.000671285,0.0002957802,0.0001155146,0.000398291,0.000004390489,0.000001218801,0.00005055773,0.9792259,0.004329465,0.009187079,0.00555779,0.0001627037],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02511225,0.00002079529,0.9662245,0.001300784,0.0007639102,0.0006418969,0.0005941099,0.000152625,0.005189138],"genre_scores_gemma":[0.8645734,0.0002671811,0.1329027,0.00134328,0.0002567851,0.00009883406,0.0003211021,0.00002877522,0.0002078679],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8798427,"threshold_uncertainty_score":0.5310625,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05317187580645688,"score_gpt":0.3378077480093347,"score_spread":0.2846358722028778,"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."}}