{"id":"W3183946154","doi":"10.32920/22734317.v1","title":"ECG Heartbeat Classification Using Multimodal Fusion","year":2023,"lang":"en","type":"preprint","venue":"","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Heartbeat; Artificial intelligence; Computer science; Convolutional neural network; Pattern recognition (psychology); Support vector machine; Deep learning","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":[0.0002109081,0.0001823101,0.0003698338,0.000253411,0.00008729329,0.00003897345,0.00007483903,0.0003109743,0.0001047068],"category_scores_gemma":[0.00008362668,0.0001536971,0.0002444218,0.0001809492,0.00002478166,0.00002100974,0.000230068,0.0004842255,0.0002343768],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001539647,"about_ca_system_score_gemma":0.0001165116,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001427175,"about_ca_topic_score_gemma":0.00001867859,"domain_scores_codex":[0.9987261,0.00003570494,0.00028615,0.0004594281,0.0003057706,0.0001868084],"domain_scores_gemma":[0.9990489,0.00003859059,0.00009760082,0.0005703367,0.0001257113,0.0001188984],"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":[0.0002020166,0.0007191299,0.6050522,0.002140851,0.001247397,0.000205421,0.0006954933,0.01202791,0.2928247,0.00007449642,0.009448744,0.07536157],"study_design_scores_gemma":[0.0003410653,0.00002840526,0.06136849,0.0006212488,0.0004805686,0.000008348545,0.0002845792,0.9333484,0.002655057,0.0001143575,0.0005283555,0.0002211935],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9771087,0.0001133467,0.01580991,0.002952745,0.001272185,0.0003297873,0.000009619122,0.0006304847,0.001773218],"genre_scores_gemma":[0.9511685,0.0001652281,0.03011183,0.0000853551,0.001068696,0.00001688788,0.0002290898,0.00005288198,0.01710154],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9213204,"threshold_uncertainty_score":0.6267584,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2024961594862609,"score_gpt":0.4001060265079818,"score_spread":0.1976098670217209,"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."}}