{"id":"W2138680037","doi":"","title":"Empirical mode decomposition for respiratory and heart rate estimation from the photoplethysmogram","year":2013,"lang":"en","type":"article","venue":"Computing in Cardiology Conference","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":58,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Photoplethysmogram; Autoregressive model; Pulse oximetry; Hilbert–Huang transform; Respiratory rate; SIGNAL (programming language); Mathematics; Heart rate; Speech recognition; Medicine; Computer science; Statistics; Internal medicine; Anesthesia; White noise; Telecommunications; Blood pressure","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":[0.001260723,0.0009093085,0.0005860081,0.0008702372,0.0001433011,0.0005930999,0.0006432355,0.0006504823,0.002041464],"category_scores_gemma":[0.003584592,0.0003293376,0.0007202231,0.0008823255,0.0002034239,0.0006366671,0.0006192615,0.00115988,0.001657212],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001578809,"about_ca_system_score_gemma":0.0003692259,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009993232,"about_ca_topic_score_gemma":0.0009116716,"domain_scores_codex":[0.9994258,0.0001532566,0.00004711799,0.0001448644,0.0001995119,0.00002956765],"domain_scores_gemma":[0.9992372,0.0003807362,0.00008329373,0.0001006755,0.0001751489,0.00002301833],"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":[0.0001966155,0.000113474,0.002256111,0.0003568804,0.000124503,0.0002212419,0.0001604492,0.06161292,0.06197441,0.004770145,0.004164623,0.8640487],"study_design_scores_gemma":[0.00002106588,0.0001056587,0.006430525,0.0000596871,0.00003369348,0.0003477792,0.00003930341,0.9655669,0.01235433,0.004482892,0.01050859,0.00004950717],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003809683,0.0004831124,0.994935,0.00003029961,0.00003316577,0.00003103057,0.0001226574,0.0003184854,0.0002366205],"genre_scores_gemma":[0.04952964,0.0008502069,0.9468022,0.00004861621,0.00009176842,0.0001925436,0.0008687884,0.0001931631,0.001423134],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002041464,"threshold_uncertainty_score":0.006829381,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03791425157203998,"score_gpt":0.3344252593904544,"score_spread":0.2965110078184144,"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."}}