{"id":"W2954201390","doi":"10.1109/access.2019.2937357","title":"An Enhanced Electrocardiogram Biometric Authentication System Using Machine Learning","year":2019,"lang":"en","type":"article","venue":"IEEE Access","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":81,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Horizon 2020 Framework Programme; Khalifa University of Science, Technology and Research; Concordia University","keywords":"Computer science; Biometrics; Security token; Password; Authentication (law); Alphanumeric; Identification (biology); Artificial intelligence; Computer security; Machine learning; Data mining","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002367891,0.0001085901,0.0002740035,0.0006346985,0.00008674158,0.00009630978,0.0001534609,0.00006650064,0.00001857296],"category_scores_gemma":[0.00002629203,0.00009465712,0.0001078869,0.0017068,0.00001058607,0.0002119792,0.00001450002,0.000167376,0.00006596957],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001174726,"about_ca_system_score_gemma":0.00002814063,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003421231,"about_ca_topic_score_gemma":0.000001577058,"domain_scores_codex":[0.9990139,0.00006136418,0.0001881445,0.000265381,0.000255481,0.0002157344],"domain_scores_gemma":[0.9993319,0.00002891096,0.0001095507,0.0003224782,0.0001118472,0.00009535091],"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":[0.00002990705,0.00005994172,0.2498284,0.0001696412,0.000146281,0.000004677658,0.00006489728,0.0008609546,0.7409552,0.000005814803,0.000002117549,0.007872172],"study_design_scores_gemma":[0.001145455,0.0003867456,0.02649337,0.0003246489,0.0007721498,0.00003879725,0.000225078,0.4495417,0.520516,0.000007449681,0.0002291579,0.0003194801],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9757828,0.0002287216,0.02286804,0.00001330736,0.0003416777,0.0001509313,9.984176e-7,0.0001888921,0.0004246684],"genre_scores_gemma":[0.9987178,0.00002173675,0.0005208262,0.00001327844,0.000343204,0.000006507364,0.00002745849,0.00002419335,0.0003249227],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4486807,"threshold_uncertainty_score":0.3860005,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02480292391664383,"score_gpt":0.3449681272661304,"score_spread":0.3201652033494865,"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."}}