{"id":"W2998351977","doi":"10.1016/j.cmpb.2019.105304","title":"A singular spectrum analysis-based model-free electrocardiogram denoising technique","year":2020,"lang":"en","type":"article","venue":"Computer Methods and Programs in Biomedicine","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":29,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"Ryerson University","keywords":"Noise reduction; Singular value decomposition; Computer science; SIGNAL (programming language); Pattern recognition (psychology); Artificial intelligence; Dependency (UML); Dimension (graph theory); Noise (video); Algorithm; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004264265,0.0005356465,0.0006935413,0.0004607108,0.0002428899,0.0005012684,0.0005849124,0.0008188062,0.001559677],"category_scores_gemma":[0.0009628104,0.0002687161,0.0009448639,0.000426616,0.0003096325,0.0006020201,0.0006497917,0.00100189,0.00139208],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001389303,"about_ca_system_score_gemma":0.0004651,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008705877,"about_ca_topic_score_gemma":0.001286921,"domain_scores_codex":[0.9997159,0.00004807679,0.00001460064,0.00006299333,0.0001417141,0.00001670429],"domain_scores_gemma":[0.999775,0.00006121597,0.00001917608,0.00005197097,0.00007758783,0.0000151056],"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":[0.0002544246,0.0001299859,0.0005719731,0.0001979518,0.0001472122,0.0001668569,0.0001054169,0.05739046,0.2437314,0.01684841,0.003069396,0.6773865],"study_design_scores_gemma":[0.00001480224,0.0001027323,0.0007030435,0.00001813026,0.00006683348,0.00045817,0.00001420935,0.9579976,0.03013481,0.004438791,0.006025295,0.00002563195],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002926961,0.0001338244,0.9961503,0.00004754263,0.00003448464,0.000009080006,0.00001892423,0.000184205,0.0004948154],"genre_scores_gemma":[0.1223607,0.0007006413,0.870034,0.0001216867,0.0001225277,0.00004942707,0.0002786972,0.0001615463,0.006170727],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001559677,"threshold_uncertainty_score":0.005217612,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0552946438106271,"score_gpt":0.36404758655499,"score_spread":0.3087529427443629,"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."}}