{"id":"W2968699478","doi":"10.1109/isscs.2019.8801783","title":"Off-the-person ECG Biometrics Using Convolutional Neural Networks","year":2019,"lang":"en","type":"article","venue":"","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Toronto; Universitatea Tehnică „Gheorghe Asachi” din Iaşi; Nvidia","keywords":"Computer science; Biometrics; Convolutional neural network; Artificial intelligence; Identification (biology); Pattern recognition (psychology); Representation (politics); Computer vision; Wearable computer; Feature extraction; Artificial neural network","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":[0.000154166,0.00007749929,0.0001514957,0.0001727862,0.00007307407,0.00001909335,0.00005772299,0.00005721055,0.0003352635],"category_scores_gemma":[0.00003665812,0.00004939351,0.000114082,0.0008352359,0.00003345052,0.0000386096,0.00001663053,0.0001424219,0.0000658288],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005477611,"about_ca_system_score_gemma":0.00002314228,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001348107,"about_ca_topic_score_gemma":0.000001439048,"domain_scores_codex":[0.9993345,0.00001899122,0.0001081527,0.0001426662,0.0002117985,0.0001839279],"domain_scores_gemma":[0.9995472,0.00008573096,0.0000375457,0.0001861732,0.00007487547,0.00006848407],"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":[0.00003966533,0.00007807522,0.9489577,0.00002425062,0.0002097561,0.00001148801,0.00003942686,0.0070395,0.003181226,0.0001209552,0.001966404,0.03833149],"study_design_scores_gemma":[0.0003628895,0.00005431946,0.02099454,0.000015502,0.0001111647,0.00002382422,0.0001590536,0.9769802,0.0001485397,0.00000231313,0.001080087,0.0000675855],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9873199,0.0009925208,0.00745217,0.0007657215,0.0005866337,0.0000944554,9.869127e-7,0.00005224351,0.002735403],"genre_scores_gemma":[0.9902474,0.00001840854,0.001492009,0.0003243323,0.000491458,8.828315e-7,0.000009227394,0.000009729833,0.00740661],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9699407,"threshold_uncertainty_score":0.36709,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04340813131528661,"score_gpt":0.2892995475525245,"score_spread":0.2458914162372378,"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."}}