{"id":"W3157169017","doi":"10.1109/isspit51521.2020.9408699","title":"Single Channel QRS Detection Using Wavelet And Median Denoising With Adaptive Multilevel Thresholding","year":2020,"lang":"en","type":"article","venue":"","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Thresholding; QRS complex; Artificial intelligence; Pattern recognition (psychology); Computer science; Discrete wavelet transform; Wavelet transform; Noise reduction; Noise (video); Sensitivity (control systems); Wavelet; Median filter; SIGNAL (programming language); Signal-to-noise ratio (imaging); Computer vision; Engineering; Image processing; Electronic engineering; Image (mathematics); Medicine; Telecommunications; Cardiology","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":[0.00006889828,0.0001273936,0.0002211875,0.00008697138,0.0001356588,0.00003009495,0.00002510327,0.00005688183,0.000009253391],"category_scores_gemma":[0.0000597588,0.00009705942,0.0000375774,0.0002178794,0.00004425307,0.00009977703,0.00002508963,0.0001475911,0.000002485116],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005467437,"about_ca_system_score_gemma":0.00002159257,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000265561,"about_ca_topic_score_gemma":0.00004685445,"domain_scores_codex":[0.9992284,0.00001436274,0.0001295759,0.0002611653,0.0001799816,0.0001864849],"domain_scores_gemma":[0.999554,0.00003486464,0.00004947749,0.00008285279,0.00007161418,0.0002071249],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006021357,0.0001964447,0.016357,0.000176262,0.0005419677,0.0002313256,0.006403747,0.0009638416,0.7785817,0.00001322643,0.00001765438,0.1959147],"study_design_scores_gemma":[0.001608478,0.0008825011,0.002310217,0.0002832082,0.0003900725,0.00008965044,0.004511762,0.6897727,0.2998373,0.0000231966,0.00004185832,0.0002490097],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.912294,0.00008198002,0.08655317,0.000562665,0.00004170524,0.00009215251,0.000001049517,0.00008638804,0.0002868264],"genre_scores_gemma":[0.9816553,0.000008196851,0.01759342,0.0002594976,0.0003980597,0.000001870933,0.000001723582,0.00002370349,0.00005820696],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6888089,"threshold_uncertainty_score":0.3957968,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08800665815566312,"score_gpt":0.2638135353871531,"score_spread":0.17580687723149,"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."}}