{"id":"W4283765119","doi":"10.3389/felec.2022.906689","title":"Detection of Atrial Fibrillation in Compressively Sensed Electrocardiogram for Remote Monitoring","year":2022,"lang":"en","type":"article","venue":"Frontiers in Electronics","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Compute Canada","keywords":"Atrial fibrillation; Receiver operating characteristic; Artifact (error); Electrocardiography; Signal-to-noise ratio (imaging); False alarm; Medicine; Pattern recognition (psychology); Artificial intelligence; Computer science; Cardiology; Internal medicine; Telecommunications","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":[0.000628462,0.0004860559,0.0005300945,0.0004066231,0.0001674238,0.0004501085,0.000478716,0.0005190918,0.0008794772],"category_scores_gemma":[0.002102298,0.000202269,0.0002082096,0.0001869055,0.00017923,0.0004039151,0.000349573,0.0003117456,0.0004252043],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001873237,"about_ca_system_score_gemma":0.0002303727,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008446206,"about_ca_topic_score_gemma":0.001598575,"domain_scores_codex":[0.9994592,0.00009204674,0.00003737862,0.0001181676,0.0002617727,0.00003140164],"domain_scores_gemma":[0.9992006,0.000323536,0.0001047575,0.00007920572,0.0002621381,0.00002981319],"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":[0.0008741733,0.0001416485,0.008747083,0.000161869,0.00007296566,0.0001690994,0.00006587882,0.006619601,0.7417786,0.0002123688,0.0006337466,0.240523],"study_design_scores_gemma":[0.000172379,0.00258207,0.06239609,0.00006224504,0.0002818943,0.003141811,0.00007762827,0.3194766,0.6071606,0.0003974486,0.004140388,0.0001108313],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.46892,0.0009890988,0.5259321,0.0001842873,0.00006675812,0.0002106384,0.0001878536,0.002077714,0.00143159],"genre_scores_gemma":[0.7155656,0.0003504242,0.2819509,0.0002363142,0.00007286794,0.0001097023,0.0003066015,0.0000721223,0.001335596],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0008794772,"threshold_uncertainty_score":0.003323615,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01062872895876247,"score_gpt":0.2638421118390846,"score_spread":0.2532133828803221,"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."}}