{"id":"W4390993493","doi":"10.1109/biocas58349.2023.10388691","title":"A Dual Stage Resource Efficient ECG Classifier","year":2023,"lang":"en","type":"article","venue":"","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Wearable computer; Field-programmable gate array; Power consumption; Classifier (UML); Convolutional neural network; Artificial intelligence; Binary number; Wearable technology; Binary classification; Pattern recognition (psychology); Real-time computing; Embedded system; Power (physics)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002864324,0.0005977152,0.0005422625,0.0006146365,0.0003012266,0.0007523972,0.00132008,0.0006652597,0.004739578],"category_scores_gemma":[0.0006304582,0.0003204767,0.0003440577,0.0005002326,0.0001424859,0.0008278338,0.0008827182,0.0005929549,0.001944359],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004434331,"about_ca_system_score_gemma":0.0009423743,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002375411,"about_ca_topic_score_gemma":0.005823636,"domain_scores_codex":[0.9995804,0.00002863706,0.00002756672,0.00009988102,0.0001838783,0.00007969387],"domain_scores_gemma":[0.9997578,0.00004200469,0.00001958016,0.00004166108,0.000113038,0.00002594776],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007197757,0.0002951398,0.002371732,0.0001582168,0.00009347439,0.0003679038,0.00003147601,0.01708502,0.1295617,0.003608742,0.01382295,0.8318838],"study_design_scores_gemma":[0.0001210563,0.0006454769,0.004519633,0.00004430514,0.0001485178,0.001208021,0.00002781645,0.8629525,0.1031839,0.002377762,0.02471025,0.00006080673],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04239561,0.001046909,0.9459538,0.00044375,0.0003655378,0.000225512,0.0004408913,0.003300344,0.00582756],"genre_scores_gemma":[0.5605361,0.0008414915,0.4016178,0.0007329813,0.0003003556,0.0002882737,0.002017022,0.0001567692,0.03350916],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004739578,"threshold_uncertainty_score":0.01585549,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03808381806918385,"score_gpt":0.3105620862166428,"score_spread":0.2724782681474589,"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."}}