{"id":"W4313525308","doi":"10.1109/bibm55620.2022.9995552","title":"Pan-Tompkins++: A Robust Approach to Detect R-peaks in ECG Signals","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Robustness (evolution); Computer science; Algorithm; Band-pass filter; Passband; QRS complex; Noise (video); Artificial intelligence; Pattern recognition (psychology); Signal processing; Noise reduction; Detector; Filter (signal processing); Reduction (mathematics); Speech recognition; Mathematics; Telecommunications; Engineering; Computer vision; Electronic engineering","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":[0.001070181,0.002626071,0.001343828,0.002223981,0.0005109966,0.001217465,0.00160167,0.001398483,0.005926876],"category_scores_gemma":[0.003225656,0.00069447,0.001227951,0.001331829,0.0003693253,0.001046616,0.001262862,0.001146086,0.004142676],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002630396,"about_ca_system_score_gemma":0.0005714614,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001706698,"about_ca_topic_score_gemma":0.003161519,"domain_scores_codex":[0.9991159,0.0001313245,0.00008110863,0.0003053392,0.0003052603,0.00006109586],"domain_scores_gemma":[0.9993885,0.0002019746,0.000125818,0.0001117895,0.0001381433,0.00003370577],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001253171,0.0002156305,0.002453455,0.0008058865,0.0004904263,0.0006683034,0.0001324168,0.03204898,0.09936187,0.001883344,0.03639713,0.8242894],"study_design_scores_gemma":[0.0003092649,0.0007456822,0.009173789,0.0001052916,0.0002751192,0.003907587,0.0001121119,0.7668341,0.1421171,0.00612227,0.07006586,0.0002317887],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0120104,0.0015123,0.9280024,0.0001679102,0.000163647,0.0002584191,0.001424423,0.05509556,0.001364956],"genre_scores_gemma":[0.08559355,0.001010411,0.8987404,0.0003423975,0.0001493478,0.0004222267,0.005452097,0.004007446,0.00428227],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005926876,"threshold_uncertainty_score":0.01982737,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07299498389888334,"score_gpt":0.3093321178113743,"score_spread":0.2363371339124909,"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."}}