{"id":"W2134075215","doi":"10.1109/cnsr.2008.38","title":"ECG Based Recognition Using Second Order Statistics","year":2008,"lang":"en","type":"article","venue":"","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":117,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Feature extraction; Computer science; Pattern recognition (psychology); Artificial intelligence; Higher-order statistics; Autocorrelation; Linear discriminant analysis; Discrete cosine transform; Rendering (computer graphics); Detector; Signal processing; Mathematics; Statistics; Image (mathematics); Digital signal processing","routes":{"ca_aff":true,"ca_fund":true,"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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00004624491,0.00005503483,0.0001117002,0.00006720926,0.00007083636,0.000004588281,0.00001311855,0.00003306598,0.002600975],"category_scores_gemma":[0.00006255508,0.00004656928,0.00002877221,0.0001535981,0.00002208185,0.00002472612,0.00000377383,0.00006617895,0.00008522977],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002552761,"about_ca_system_score_gemma":0.00007126399,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009171857,"about_ca_topic_score_gemma":0.00001367663,"domain_scores_codex":[0.9995801,0.00001228832,0.0001060777,0.00009795806,0.0001075464,0.00009604324],"domain_scores_gemma":[0.9996359,0.00003563585,0.00002462931,0.00009569145,0.0001454099,0.00006278299],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002677064,0.001153427,0.6635984,0.0005592199,0.0006646503,0.001319805,0.0004696657,0.0004014937,0.06303331,0.0000155488,0.06845664,0.2000601],"study_design_scores_gemma":[0.00387172,0.0003644883,0.03408119,0.000206824,0.000761752,0.0002565979,0.0002987479,0.8861003,0.06436938,0.0002099665,0.008939994,0.0005390653],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8043124,0.0000220457,0.1908786,0.00009772411,0.00008708329,0.00004710809,0.00002026462,0.00005803449,0.004476784],"genre_scores_gemma":[0.5329843,0.000008067433,0.4588613,0.0005592313,0.0001894122,0.000001528316,0.0000881777,0.00001318796,0.007294751],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8856988,"threshold_uncertainty_score":0.9983108,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08491570655177896,"score_gpt":0.3080419949094669,"score_spread":0.2231262883576879,"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."}}