{"id":"W2802068140","doi":"10.1038/sdata.2018.76","title":"An optimal filter for short photoplethysmogram signals","year":2018,"lang":"en","type":"article","venue":"Scientific Data","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":230,"is_retracted":false,"has_abstract":true,"ca_institutions":"B.C. Women's Hospital & Health Centre; BC Children's Hospital; Children's & Women's Health Centre of British Columbia; University of British Columbia","funders":"Division of Graduate Education; Guilin University of Electronic Technology; National Natural Science Foundation of China","keywords":"Photoplethysmogram; Chebyshev filter; Filter (signal processing); Computer science; SIGNAL (programming language); Signal processing; Wearable computer; Noise (video); Interference (communication); Artificial intelligence; Computer vision; Telecommunications; Channel (broadcasting); Digital signal processing; Computer hardware; Embedded system","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":[0.0005174926,0.0004058169,0.0003391834,0.0005447104,0.0002766425,0.000515951,0.000212186,0.0006473045,0.001543858],"category_scores_gemma":[0.001968535,0.0001599183,0.0004223088,0.0003576565,0.0002587225,0.0005001995,0.0001416598,0.0003178086,0.0004732557],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004314451,"about_ca_system_score_gemma":0.0005773046,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001702452,"about_ca_topic_score_gemma":0.001993099,"domain_scores_codex":[0.9997024,0.00004451399,0.00002662035,0.00007061191,0.0001209137,0.00003490653],"domain_scores_gemma":[0.9991387,0.00033087,0.00008332048,0.00005092482,0.0003647072,0.00003144043],"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":[0.001761081,0.0002129666,0.005354543,0.0003759474,0.00006752052,0.0001897976,0.0001732503,0.007628135,0.7475802,0.001379384,0.0007246897,0.2345525],"study_design_scores_gemma":[0.0001330463,0.001625971,0.05308051,0.00009306715,0.0002973601,0.0009327668,0.0002810784,0.2163782,0.7187725,0.001679611,0.006626654,0.00009929639],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4085924,0.001063185,0.5878503,0.0001329211,0.000108631,0.0001055086,0.0001079364,0.0003893511,0.001649829],"genre_scores_gemma":[0.7077621,0.0004843945,0.2899442,0.00007347597,0.00003106214,0.00006350778,0.0001274208,0.00004789816,0.001466055],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001702452,"threshold_uncertainty_score":0.005164683,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06778257964544786,"score_gpt":0.3175512912259267,"score_spread":0.2497687115804789,"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."}}