{"id":"W4318053004","doi":"10.3390/diagnostics13030439","title":"Electrocardiogram (ECG)-Based User Authentication Using Deep Learning Algorithms","year":2023,"lang":"en","type":"article","venue":"Diagnostics","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Biometrics; Authentication (law); Deep learning; Convolutional neural network; Artificial intelligence; Fingerprint (computing); Artificial neural network; Fingerprint recognition; Machine learning; Data mining; Pattern recognition (psychology); Computer security","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":[0.0002181064,0.0001124665,0.0002077937,0.0002427393,0.0001420749,0.0000337354,0.00005485827,0.00008081543,0.00001622322],"category_scores_gemma":[0.00115026,0.0001097463,0.0001448872,0.0008894592,0.00002958681,0.00002918132,0.00001901822,0.0002166348,0.0001678165],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005819185,"about_ca_system_score_gemma":0.00004550518,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004116736,"about_ca_topic_score_gemma":0.000001425603,"domain_scores_codex":[0.9990149,0.00004404425,0.0001746866,0.0002044127,0.0002702122,0.0002917325],"domain_scores_gemma":[0.9991063,0.0003914016,0.00005754069,0.0002110968,0.0001250374,0.0001085481],"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":[0.00001912037,0.0001851505,0.9059746,0.0000921087,0.000286407,0.0001801668,0.0002782744,0.01446696,0.005113842,0.00003471502,0.0005797829,0.07278893],"study_design_scores_gemma":[0.0006360536,0.0001813182,0.1103517,0.0001562058,0.0008254584,0.000008040552,0.0002379985,0.874559,0.007130384,0.00005115178,0.005656329,0.000206324],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9315401,0.0003790143,0.06649329,0.0003998385,0.0003190926,0.000163801,0.000002714195,0.0005402869,0.0001618545],"genre_scores_gemma":[0.9906007,0.0003734769,0.007825776,0.00008703655,0.000460976,0.00001681801,0.0001516058,0.00003745127,0.0004461334],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.860092,"threshold_uncertainty_score":0.4475324,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02435143304729278,"score_gpt":0.3137151867159193,"score_spread":0.2893637536686265,"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."}}