{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005193346,0.0001260455,0.0003011379,0.0001530698,0.00006851654,0.00002930197,0.00002406267,0.0000674763,0.00004936487],"category_scores_gemma":[0.000231151,0.0001110124,0.00009456606,0.00035361,0.00001235664,0.00002961762,0.000009109491,0.0001433381,0.00003819442],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004100666,"about_ca_system_score_gemma":0.00003713408,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002265724,"about_ca_topic_score_gemma":0.000002456471,"domain_scores_codex":[0.9987611,0.0002701856,0.0001832797,0.0003304617,0.0002435093,0.00021153],"domain_scores_gemma":[0.9991673,0.0001081459,0.00003492853,0.0003106275,0.0001168258,0.000262229],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000700833,0.0002759934,0.003923463,0.00009295962,0.0003402679,0.0002146915,0.0004653115,0.007558791,0.6156327,0.00001936276,0.0004774535,0.3709289],"study_design_scores_gemma":[0.0007419633,0.0005856376,0.03788687,0.0002349837,0.0006165734,0.0001269767,0.0007055483,0.1131558,0.844309,0.0001188982,0.001231399,0.0002863356],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9914944,0.000112496,0.006579978,0.001227056,0.00006153556,0.0001016418,0.000001573069,0.0001212528,0.0003001178],"genre_scores_gemma":[0.9788773,0.00001879876,0.01943872,0.0005561514,0.000309307,0.000005019745,0.00001878438,0.00002152963,0.0007543809],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3706426,"threshold_uncertainty_score":0.4526953,"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."}}