{"id":"W2535940264","doi":"10.1109/bcc.2006.4341628","title":"ECG Biometric Recognition Without Fiducial Detection","year":2006,"lang":"en","type":"article","venue":"","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":245,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Biometrics; Fiducial marker; Computer science; Artificial intelligence; Heartbeat; Discrete cosine transform; Pattern recognition (psychology); Waveform; Autocorrelation; False positive rate; Computer vision; Mathematics; Computer security; Image (mathematics)","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":[0.0008426837,0.0003883701,0.0007983107,0.0009518535,0.0001816674,0.0006778719,0.0007007652,0.001047187,0.002510394],"category_scores_gemma":[0.005287861,0.0001871798,0.0003261182,0.000579567,0.0004130195,0.0009486584,0.0005345015,0.0004607253,0.004577841],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001723126,"about_ca_system_score_gemma":0.0002677086,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003211948,"about_ca_topic_score_gemma":0.000357714,"domain_scores_codex":[0.998015,0.0003632377,0.0001137723,0.0003320159,0.001103228,0.00007273436],"domain_scores_gemma":[0.9972277,0.0007310907,0.0004378479,0.0009797929,0.0005582851,0.00006526308],"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":[0.0005821348,0.00008739065,0.005496836,0.0003007627,0.00006488348,0.000396092,0.00008160112,0.001311378,0.256356,0.003462674,0.003969189,0.727891],"study_design_scores_gemma":[0.000114547,0.002124953,0.05681744,0.0002240284,0.0002502325,0.0310024,0.0001742576,0.1652736,0.6634378,0.005675308,0.07467727,0.0002281869],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1173425,0.00402625,0.8624482,0.0006596139,0.0008183869,0.0001408995,0.0004171555,0.004234365,0.009912654],"genre_scores_gemma":[0.6453399,0.001887076,0.3361553,0.0004740467,0.0003916009,0.00009363276,0.0006008838,0.00018064,0.01487697],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002510394,"threshold_uncertainty_score":0.008398116,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01950612856830704,"score_gpt":0.2675946561948083,"score_spread":0.2480885276265012,"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."}}