{"id":"W4402352315","doi":"10.1109/jbhi.2024.3455803","title":"ECG Biometric Authentication Using Self-Supervised Learning for IoT Edge Sensors","year":2024,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"China Scholarship Council","keywords":"Computer science; Biometrics; Convolutional neural network; Generalizability theory; Artificial intelligence; Deep learning; Machine learning; Authentication (law); Enhanced Data Rates for GSM Evolution; Edge device; Data mining; Wearable computer; Pattern recognition (psychology); Real-time computing; Cloud computing; Embedded system","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001582407,0.0001026201,0.0003777205,0.001088351,0.0001551556,0.00005831231,0.00005196753,0.0001022793,0.000009404005],"category_scores_gemma":[0.0001442821,0.00007128676,0.000138883,0.00086867,0.00005406348,0.0001050933,0.00001088395,0.0003553899,0.000004949409],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001444922,"about_ca_system_score_gemma":0.0005262281,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009476984,"about_ca_topic_score_gemma":1.363589e-7,"domain_scores_codex":[0.998191,0.00002504021,0.001077524,0.00005922788,0.0004070302,0.0002401581],"domain_scores_gemma":[0.9988346,0.0001483756,0.0003298725,0.00006581779,0.0001809645,0.0004404445],"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":[0.0001444195,0.0004938612,0.006166148,0.02070221,0.001181162,0.00005214215,0.01753781,0.000193712,0.002578314,0.00006993502,0.007302676,0.9435776],"study_design_scores_gemma":[0.001012363,0.001150722,0.0005569943,0.001115095,0.0003621503,0.0004850203,0.00134397,0.9627408,0.0001763376,0.00005740827,0.03089855,0.0001005533],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8814725,0.00272565,0.1067055,0.007546025,0.001247063,0.0002059358,0.000006326842,0.0000647211,0.00002630803],"genre_scores_gemma":[0.8390535,0.002227382,0.1558869,0.0005827227,0.002034582,0.000002127908,0.00001294596,0.00002363875,0.0001762842],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9625471,"threshold_uncertainty_score":0.290699,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06589994642052614,"score_gpt":0.3801596880879599,"score_spread":0.3142597416674337,"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."}}