{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007642447,0.00007521395,0.0001594203,0.00006916345,0.0001005873,0.00006188705,0.00006260286,0.00006441018,0.00009705349],"category_scores_gemma":[0.00005081042,0.00006787221,0.00008272356,0.0003158257,0.00001707164,0.0001381102,0.00002828149,0.0001130602,0.00003224108],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005569831,"about_ca_system_score_gemma":0.00007353173,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001695079,"about_ca_topic_score_gemma":0.00001155278,"domain_scores_codex":[0.9992872,0.00003101042,0.0001462031,0.0002146441,0.0001859965,0.0001349529],"domain_scores_gemma":[0.9994261,0.00002610635,0.00004324834,0.0002731456,0.0001494983,0.00008193193],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002757249,0.0001640599,0.2895565,0.00006388631,0.00006050852,0.00009900369,0.00007386529,0.00034473,0.6845732,0.000004782487,0.0005842209,0.02444761],"study_design_scores_gemma":[0.001172198,0.00003765963,0.2088904,0.0002861155,0.0003747964,0.00009458495,0.0002690307,0.3108256,0.475167,0.00005365563,0.002585748,0.0002432412],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9929262,0.0001495353,0.004895845,0.0007780701,0.0004332015,0.00005022964,0.0000016197,0.0000532589,0.0007120018],"genre_scores_gemma":[0.9953892,0.00005264933,0.002775663,0.000212879,0.0005613059,0.000003296877,0.00001830951,0.00001292004,0.0009737342],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3104809,"threshold_uncertainty_score":0.2767749,"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."}}