{"id":"W4388555293","doi":"10.48550/arxiv.2311.04229","title":"Exploring Best Practices for ECG Pre-Processing in Machine Learning","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research; Heart and Stroke Foundation of Canada","keywords":"Normalization (sociology); Computer science; Artificial intelligence; Machine learning; Signal processing; Sampling (signal processing); Pattern recognition (psychology); Data mining; Speech recognition; Digital signal processing; Filter (signal processing); Computer vision","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01629842,0.001781849,0.001290004,0.002798488,0.0008997982,0.004051478,0.003170072,0.00237913,0.001618319],"category_scores_gemma":[0.06558646,0.0009813663,0.0009309265,0.001928835,0.001762534,0.005340646,0.002011498,0.004354753,0.001760686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001327024,"about_ca_system_score_gemma":0.002219954,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003487167,"about_ca_topic_score_gemma":0.004546532,"domain_scores_codex":[0.9871268,0.005726928,0.001083051,0.002394464,0.003177848,0.0004909883],"domain_scores_gemma":[0.9627239,0.02290159,0.001729376,0.006559923,0.005674033,0.0004111287],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008956788,0.0006706617,0.008233155,0.001647149,0.0003672313,0.0001403327,0.0006158887,0.06965453,0.019544,0.01841787,0.008245307,0.8715682],"study_design_scores_gemma":[0.0004578873,0.002256833,0.01538232,0.005011508,0.0006206332,0.001075604,0.00191352,0.5282981,0.1678164,0.1991817,0.07751654,0.0004690365],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04786457,0.03440605,0.8981171,0.009568168,0.0004493905,0.0002185447,0.000295452,0.003834623,0.005246005],"genre_scores_gemma":[0.2834125,0.01168099,0.699882,0.001368659,0.0003802516,0.0002348097,0.0008702486,0.000761471,0.001409125],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01629842,"threshold_uncertainty_score":0.08619529,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4916361074348901,"score_gpt":0.297468015869145,"score_spread":0.194168091565745,"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."}}