{"id":"W1552405914","doi":"10.1109/icassp.2015.7178283","title":"Posture-invariant ECG recognition with posture detection","year":2015,"lang":"en","type":"article","venue":"","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Biometrics; Computer science; Body posture; Artificial intelligence; Computer vision; Modality (human–computer interaction); Context (archaeology); Invariant (physics); Pattern recognition (psychology); Speech recognition; Mathematics; Physical medicine and rehabilitation; Medicine","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":[0.0003639866,0.0004743101,0.0005338539,0.0006601652,0.0001217516,0.0003843767,0.0004365671,0.0006923262,0.001428761],"category_scores_gemma":[0.001031138,0.000178892,0.0003653999,0.0004968361,0.0001891623,0.0003446698,0.000409834,0.0002895275,0.001468078],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001052584,"about_ca_system_score_gemma":0.0001480481,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003905581,"about_ca_topic_score_gemma":0.0006242368,"domain_scores_codex":[0.9995537,0.00008721912,0.00003269348,0.0001259825,0.0001623361,0.00003812396],"domain_scores_gemma":[0.9996616,0.00007165621,0.00006135192,0.00008152876,0.00009946441,0.0000243329],"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":[0.0003508169,0.0001381606,0.003483093,0.0001630209,0.0000576149,0.0002904884,0.00004276136,0.002659105,0.4336913,0.0005067242,0.00157328,0.5570436],"study_design_scores_gemma":[0.00008802636,0.001533852,0.1166729,0.00008828884,0.0002257088,0.008346059,0.00007583818,0.4008887,0.4576741,0.002078517,0.012186,0.0001418566],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.126438,0.001973202,0.8632045,0.0002877603,0.0004928178,0.0001768286,0.0003894566,0.0028151,0.004222151],"genre_scores_gemma":[0.7029216,0.001309379,0.2886512,0.000333902,0.0002752708,0.0001037491,0.0005460397,0.0001288241,0.005729957],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001428761,"threshold_uncertainty_score":0.004779637,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0333079357016598,"score_gpt":0.2567792804448313,"score_spread":0.2234713447431715,"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."}}