{"id":"W4205669528","doi":"10.1109/access.2021.3138976","title":"Deep Learning Models for Magnetic Cardiography Edge Sensors Implementing Noise Processing and Diagnostics","year":2021,"lang":"en","type":"article","venue":"IEEE Access","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University; Thunder Bay Regional Research Institute","funders":"Tohoku University; Qatar National Research Fund; Ministry of Economy, Trade and Industry; Fonds National de la Recherche Luxembourg; Qatar Foundation","keywords":"Computer science; Deep learning; Artificial intelligence; Noise (video); Edge computing; Pipeline (software); Noise reduction; Machine learning; Edge device; Real-time computing; Enhanced Data Rates for GSM Evolution","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":[0.0001816041,0.0001310215,0.0002893523,0.0001421319,0.0002632128,0.0001774624,0.00006475784,0.00005557467,0.000006989159],"category_scores_gemma":[0.0001668662,0.0001272261,0.0001398088,0.0004053774,0.00003110298,0.0001768939,0.00005551991,0.0001533682,7.202443e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001466799,"about_ca_system_score_gemma":0.00003745006,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003739823,"about_ca_topic_score_gemma":0.00001099733,"domain_scores_codex":[0.9989559,0.00003171658,0.0002169094,0.0003061074,0.0001686758,0.0003206927],"domain_scores_gemma":[0.9992251,0.0001674533,0.00007447827,0.0001480975,0.0002712158,0.0001136307],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004059581,0.0001045196,0.6622511,0.001312051,0.0002959876,0.0001726219,0.001071138,0.01913913,0.004836033,0.00001418069,0.0002733809,0.3104893],"study_design_scores_gemma":[0.004737323,0.0003689594,0.05655342,0.001356165,0.004623535,0.0001576894,0.004147665,0.8744522,0.04419322,0.001187496,0.007138259,0.001084091],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9542882,0.007609205,0.03690256,0.0001711111,0.0001585291,0.0001745948,0.000003948742,0.00007757179,0.0006142138],"genre_scores_gemma":[0.9947968,0.001062681,0.003192241,0.00006765133,0.0005162707,0.00003813962,0.00001909564,0.00003057063,0.0002765299],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8553131,"threshold_uncertainty_score":0.5188128,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03766770326564362,"score_gpt":0.3237624697744021,"score_spread":0.2860947665087585,"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."}}