{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003431838,0.0005123959,0.0004475937,0.0007513099,0.0001121252,0.0002419699,0.0003991876,0.0004087638,0.0009847957],"category_scores_gemma":[0.0006012574,0.0001242257,0.0003723394,0.0002983607,0.00007554732,0.0003486471,0.0003584775,0.0004318037,0.000538611],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002385545,"about_ca_system_score_gemma":0.0002316071,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002686431,"about_ca_topic_score_gemma":0.004408037,"domain_scores_codex":[0.9998384,0.00001933865,0.00001292207,0.00005287375,0.00003501898,0.00004149872],"domain_scores_gemma":[0.9998264,0.00003616954,0.00002433246,0.00001598378,0.000075808,0.00002141377],"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":[0.0009593741,0.0007066922,0.05581703,0.0001645486,0.0001751895,0.0003634655,0.0001133056,0.05540961,0.04785334,0.0006636176,0.008973275,0.8288006],"study_design_scores_gemma":[0.0000319171,0.0003252387,0.03014293,0.00003322564,0.0000688921,0.0002235538,0.00004403434,0.9504672,0.0156174,0.000836899,0.002187189,0.00002147845],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7788473,0.002305736,0.2087251,0.0004022399,0.000415379,0.0001389969,0.002140853,0.00266429,0.004360059],"genre_scores_gemma":[0.9556361,0.0005573221,0.0356268,0.0001721269,0.0001024406,0.00005641086,0.003152928,0.00003165492,0.00466428],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002686431,"threshold_uncertainty_score":0.005341589,"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."}}