{"id":"W4200047863","doi":"10.1109/embc46164.2021.9630599","title":"ECG Dry-electrode 3D Printing and Signal Quality Considerations","year":2021,"lang":"en","type":"article","venue":"2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"SIGNAL (programming language); Electrode; Kurtosis; QRS complex; Computer science; Noise (video); Biomedical engineering; Signal-to-noise ratio (imaging); Quality (philosophy); Electrical conductor; Materials science; Artificial intelligence; Electrical engineering; Telecommunications; Medicine; Engineering; Mathematics; Cardiology; Statistics; Chemistry","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":[0.001139012,0.0006271878,0.0005006519,0.0006546664,0.0001738902,0.0009461764,0.0008258641,0.0009460313,0.002704285],"category_scores_gemma":[0.004135977,0.0002700939,0.0004858516,0.0005409048,0.0004265093,0.0006684462,0.0004124175,0.0004946855,0.0007898397],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002404413,"about_ca_system_score_gemma":0.0001952259,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002069078,"about_ca_topic_score_gemma":0.0002444345,"domain_scores_codex":[0.9986719,0.0002072983,0.00009786899,0.0001925133,0.0007868754,0.00004357584],"domain_scores_gemma":[0.9973858,0.001122265,0.0003259938,0.0003666282,0.0007521611,0.00004710175],"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":[0.0002574347,0.00006216716,0.001178911,0.0005595386,0.00003193159,0.0007981695,0.0001441575,0.003590401,0.9261963,0.0007073085,0.0009944289,0.06547919],"study_design_scores_gemma":[0.00004485132,0.001465183,0.008061543,0.00004511989,0.00007992372,0.003273576,0.00009242755,0.02402579,0.9473175,0.0006474955,0.01487279,0.00007375688],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2393494,0.003647704,0.7451593,0.00075378,0.0007136309,0.000503892,0.001020826,0.002067546,0.006784033],"genre_scores_gemma":[0.6515524,0.002140346,0.335833,0.0004514311,0.0001842982,0.000381634,0.001030784,0.0003797592,0.008046371],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002704285,"threshold_uncertainty_score":0.009046733,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05873433801116049,"score_gpt":0.3499461404648968,"score_spread":0.2912118024537363,"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."}}