{"id":"W4200151102","doi":"10.3390/s21248169","title":"An Automatic Method to Reduce Baseline Wander and Motion Artifacts on Ambulatory Electrocardiogram Signals","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Wearable computer; Artificial intelligence; Noise (video); Baseline (sea); Wearable technology; Mobile device; Motion sensors; Medical diagnosis; SIGNAL (programming language); Ambulatory ECG; Ambulatory; Computer vision; Noise reduction; Motion (physics); Real-time computing; Medicine; Embedded system","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":[0.0005358485,0.0009736898,0.0007511199,0.001311813,0.0004023042,0.0005768267,0.0008428278,0.001024757,0.001052304],"category_scores_gemma":[0.001835264,0.000299608,0.0007120099,0.000840631,0.00031513,0.0006309006,0.0004311081,0.0006777786,0.0007732552],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001794968,"about_ca_system_score_gemma":0.0007318506,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001475867,"about_ca_topic_score_gemma":0.002268561,"domain_scores_codex":[0.9992769,0.00007140439,0.00005843791,0.0001882377,0.0003691548,0.00003578211],"domain_scores_gemma":[0.999164,0.0002116998,0.00009667875,0.00009568081,0.0003984621,0.00003352839],"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":[0.0002225219,0.0002038522,0.001183773,0.0001604247,0.00008923106,0.0001917803,0.00008608366,0.005749604,0.2181254,0.0007703698,0.002429823,0.7707871],"study_design_scores_gemma":[0.0002356851,0.001304374,0.02942469,0.00007094725,0.0003890535,0.005051413,0.0001088212,0.6347584,0.3040471,0.001250011,0.02317185,0.0001875978],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0294973,0.0005994092,0.9673903,0.0001002353,0.0001515032,0.00009448796,0.00006758793,0.001471845,0.0006273832],"genre_scores_gemma":[0.1078599,0.0004557242,0.8877835,0.0001431416,0.0001592278,0.000122994,0.000377311,0.0001872396,0.00291091],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001475867,"threshold_uncertainty_score":0.00352025,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02190593309165518,"score_gpt":0.3419431074636854,"score_spread":0.3200371743720302,"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."}}