{"id":"W4224032386","doi":"10.22215/etd/2022-14931","title":"Analysis of Compressively Sensed Electrocardiogram for Detection of Atrial Fibrillation","year":2022,"lang":"en","type":"dissertation","venue":"","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Ambulatory ECG; False positive paradox; Compressed sensing; Artificial intelligence; Atrial fibrillation; Pattern recognition (psychology); Computer science; Artifact (error); Electrocardiography; Medicine; Cardiology","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":[],"consensus_categories":[],"category_scores_codex":[0.0001778637,0.0001411181,0.0009824468,0.001150289,0.0000607521,0.000004773165,0.00004095026,0.0001598878,0.00008173072],"category_scores_gemma":[0.0001250152,0.0001348209,0.002002209,0.001414735,0.000009844324,0.00001805818,0.000005335649,0.0001160553,2.166789e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006295674,"about_ca_system_score_gemma":0.00007505911,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004858474,"about_ca_topic_score_gemma":0.0001230831,"domain_scores_codex":[0.9987216,0.00004755603,0.0005055175,0.0002390786,0.0003727487,0.0001134792],"domain_scores_gemma":[0.9985893,0.0001662516,0.0005886473,0.0002170269,0.0004038425,0.00003489238],"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":[0.009591924,0.0001025909,0.04256146,0.001885108,0.0804433,0.000003175561,0.0009234012,0.02726737,0.6285763,0.0000346086,0.0001116609,0.2084991],"study_design_scores_gemma":[0.003972798,0.002452526,0.1094111,0.0001870346,0.1827669,0.000003198062,0.003322202,0.1521917,0.5414954,0.00005273705,0.003559646,0.0005847095],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9938282,0.0008456339,0.003421417,0.000007746553,0.0003393297,0.0004526351,0.00006541464,0.00004353423,0.0009960802],"genre_scores_gemma":[0.9912395,0.000141192,0.0004173087,0.00000166183,0.0003539914,0.00001162207,0.004482234,0.0000214208,0.003331093],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2079144,"threshold_uncertainty_score":0.5497838,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01319113920569523,"score_gpt":0.3178923809011475,"score_spread":0.3047012416954523,"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."}}