{"id":"W4399097124","doi":"10.3389/frsip.2024.1396077","title":"Enhancement of single-lead dry-electrode ECG through wavelet denoising","year":2024,"lang":"en","type":"article","venue":"Frontiers in Signal Processing","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Lead (geology); Wavelet; Noise reduction; Artificial intelligence; Pattern recognition (psychology); Computer science; Environmental science; Geology","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":[0.0004831117,0.0004750673,0.0003937161,0.0004258539,0.00009312328,0.0004133441,0.000319548,0.0005584757,0.001441226],"category_scores_gemma":[0.001312547,0.0001576523,0.0004087014,0.0004630024,0.0001976694,0.0005202712,0.0002834709,0.0003371443,0.0006447923],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009243929,"about_ca_system_score_gemma":0.0001239673,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002321727,"about_ca_topic_score_gemma":0.0004216788,"domain_scores_codex":[0.9997796,0.00003902741,0.00001434835,0.00003654766,0.0001169117,0.00001355875],"domain_scores_gemma":[0.9996198,0.0001371872,0.00004369747,0.0000527727,0.0001322549,0.00001420589],"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":[0.0005878102,0.0001097072,0.00278238,0.0005549343,0.00006254757,0.0009151162,0.0002055346,0.01355534,0.6385036,0.001592295,0.001946375,0.3391843],"study_design_scores_gemma":[0.00007307545,0.001480905,0.02742914,0.000149287,0.0002222771,0.006113543,0.0002439424,0.3674973,0.5724174,0.003039283,0.02124225,0.0000915689],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.195453,0.001240454,0.7971975,0.0003047974,0.0001716701,0.00007195665,0.0002628252,0.0008766726,0.004421177],"genre_scores_gemma":[0.5539921,0.002620027,0.436788,0.0002112917,0.0001341088,0.00007242084,0.0005013162,0.00023936,0.005441353],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001441226,"threshold_uncertainty_score":0.00482142,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01938552287963988,"score_gpt":0.2816592643352767,"score_spread":0.2622737414556368,"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."}}