{"id":"W4297786984","doi":"10.1109/icecet55527.2022.9872674","title":"Age Classification Based on ECG QRS Wave Using Deep Learning","year":2022,"lang":"en","type":"article","venue":"2022 International Conference on Electrical, Computer and Energy Technologies (ICECET)","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"QRS complex; Artificial intelligence; Pattern recognition (psychology); Electrocardiography; Estimator; Biometrics; Computer science; Speech recognition; Medicine; Mathematics; Statistics; Cardiology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001552198,0.0001971481,0.0002640923,0.0006992557,0.0003619647,0.000086535,0.000275755,0.0001084266,0.0001391888],"category_scores_gemma":[0.00008073341,0.0001858888,0.0001034663,0.000542742,0.00007683331,0.00004447559,0.0001815791,0.0007455942,0.00000318461],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003461189,"about_ca_system_score_gemma":0.00006728811,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006576325,"about_ca_topic_score_gemma":0.000002821437,"domain_scores_codex":[0.9983714,0.00008388212,0.0002519056,0.0004942787,0.0005381274,0.0002603888],"domain_scores_gemma":[0.9993168,0.0001177518,0.0001348246,0.0002604574,0.0001184198,0.00005181347],"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":[0.000250375,0.0004349512,0.004173727,0.00001144993,0.0002040534,0.0001793993,0.00004672821,0.01565874,0.005154252,0.03705566,0.0005288011,0.9363019],"study_design_scores_gemma":[0.0004815284,0.0009196369,0.0008788658,0.00003298246,0.00004202234,0.00003101097,0.0002444492,0.9889027,0.0006816598,0.0008013356,0.006794963,0.0001888644],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4243703,0.0006162935,0.5094195,0.02247394,0.00197942,0.0003795097,0.00002483301,0.003721856,0.03701431],"genre_scores_gemma":[0.9943632,0.0002644159,0.003744943,0.0004550161,0.0001146171,0.00005151816,0.0001028063,0.00001903977,0.0008844839],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.973244,"threshold_uncertainty_score":0.7580323,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05107929878244313,"score_gpt":0.2821552716094956,"score_spread":0.2310759728270524,"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."}}