{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001971609,0.00006689625,0.0001356641,0.0001392881,0.00005868001,0.00001312678,0.00003079566,0.00004265129,0.000321159],"category_scores_gemma":[0.00006553958,0.00004882332,0.00008860194,0.0004952537,0.00002343124,0.000007201813,0.00003554499,0.0001052889,0.0009300784],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002517602,"about_ca_system_score_gemma":0.00002213192,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003699912,"about_ca_topic_score_gemma":0.000002631971,"domain_scores_codex":[0.9992695,0.00001604909,0.0001166512,0.0001675823,0.0002350696,0.0001951041],"domain_scores_gemma":[0.9995682,0.00004793967,0.00001801767,0.0002297289,0.00002704784,0.0001090684],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003763511,0.001033759,0.2922508,0.0004688459,0.001095467,0.002875383,0.00321268,0.006981619,0.0613542,0.001890288,0.4626459,0.1658147],"study_design_scores_gemma":[0.002112345,0.000295417,0.07375394,0.000149978,0.0003125067,0.00002407937,0.006538005,0.2552669,0.01162397,0.00001639785,0.6495361,0.0003703594],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9387994,0.00002520309,0.0002199775,0.002117472,0.00009528757,0.00005794685,0.00000257532,0.0003776204,0.05830456],"genre_scores_gemma":[0.7015424,0.000005229529,0.0002267742,0.0001567133,0.0002197857,0.000004203853,0.0000127374,0.00001069298,0.2978214],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2482853,"threshold_uncertainty_score":0.9998478,"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."}}