{"id":"W3184833890","doi":"10.1109/access.2021.3097614","title":"ECG Heartbeat Classification Using Multimodal Fusion","year":2021,"lang":"en","type":"article","venue":"IEEE Access","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":149,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Heartbeat; Convolutional neural network; Pattern recognition (psychology); Support vector machine; Deep learning; Feature extraction; Machine 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005034577,0.0006948622,0.000631659,0.001347428,0.0001702062,0.0005843265,0.0004118238,0.0006327394,0.001406263],"category_scores_gemma":[0.00153438,0.000113585,0.000611001,0.0006623066,0.0001835861,0.0006959465,0.0007897486,0.0005061569,0.000576166],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002879644,"about_ca_system_score_gemma":0.0002797481,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00130836,"about_ca_topic_score_gemma":0.001384455,"domain_scores_codex":[0.999635,0.00005552885,0.00002097513,0.0001041714,0.0001234812,0.00006091386],"domain_scores_gemma":[0.9997277,0.00006018302,0.00004240143,0.00003917603,0.0001032514,0.00002732779],"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":[0.0008572624,0.0002634163,0.01124231,0.0001204438,0.0001472879,0.0003212049,0.00008859226,0.04997557,0.0797782,0.001423376,0.004860902,0.8509215],"study_design_scores_gemma":[0.00002965398,0.0002704005,0.01274382,0.00002895213,0.00009377162,0.000440428,0.00006738174,0.9494561,0.03206704,0.002570787,0.002192206,0.00003946485],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2566743,0.001792315,0.7305226,0.0005827486,0.0001992677,0.0001664239,0.001108262,0.00365807,0.005295944],"genre_scores_gemma":[0.9122215,0.0004423842,0.08422533,0.0001698006,0.0001381887,0.00006540013,0.001189295,0.00005588531,0.001492232],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001406263,"threshold_uncertainty_score":0.004704356,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1327671928979858,"score_gpt":0.4148129193904428,"score_spread":0.282045726492457,"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."}}