{"id":"W4405099122","doi":"10.22215/etd/2024-16267","title":"Toward Robust Automated Cardiovascular Arrhythmia Detection using Self-supervised Learning and 1-Dimensional Vision Transformers","year":2024,"lang":"en","type":"dissertation","venue":"","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Heartbeat; Artificial intelligence; Machine learning; Computer science; Training set; Software deployment; Noise (video); Supervised learning; Process (computing); Transformer; Data mining; Engineering; Computer security; Artificial neural network","routes":{"ca_aff":true,"ca_fund":false,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003272656,0.0003711541,0.0007301497,0.000511741,0.0002168134,0.0000843856,0.00002856595,0.0004726242,0.00005780275],"category_scores_gemma":[0.00003097546,0.0003169669,0.0006809233,0.0004710404,0.00001679145,0.00009676826,0.00001035068,0.0007079198,0.00002476518],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000184618,"about_ca_system_score_gemma":0.0001395741,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004380353,"about_ca_topic_score_gemma":0.00002532072,"domain_scores_codex":[0.9980426,0.00006988856,0.0003788106,0.000612789,0.0006239364,0.0002719505],"domain_scores_gemma":[0.9994096,0.0000320473,0.00006077726,0.0001435126,0.0001892276,0.0001648333],"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":[0.001135063,0.0002020528,0.001062127,0.01578753,0.02560878,0.001565307,0.007814417,0.0490127,0.2218418,0.000004769158,0.0001336133,0.6758319],"study_design_scores_gemma":[0.0009070174,0.0002229759,0.000626435,0.001586027,0.00751121,0.0002647446,0.002495938,0.9742002,0.01047536,0.000003995221,0.001256583,0.000449497],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9888283,0.005978957,0.000858416,0.00004796166,0.0008568459,0.0003034295,0.000002115915,0.001453602,0.001670383],"genre_scores_gemma":[0.9896858,0.0007351057,0.004817741,0.00001006531,0.0006709627,0.00001500706,0.0004904919,0.00011888,0.003455974],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9251875,"threshold_uncertainty_score":0.9999282,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01514532134259339,"score_gpt":0.2767671070254795,"score_spread":0.2616217856828861,"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."}}