{"id":"W2522264526","doi":"10.3390/bioengineering3040021","title":"Optimal Signal Quality Index for Photoplethysmogram Signals","year":2016,"lang":"en","type":"article","venue":"Bioengineering","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":308,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"BC Children's Hospital; Children's Hospital Foundation","keywords":"Photoplethysmogram; Kurtosis; Computer science; Artificial intelligence; SIGNAL (programming language); Pattern recognition (psychology); Mahalanobis distance; Linear discriminant analysis; Smoothing; Mathematics; Statistics; Computer vision; Filter (signal processing)","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002759136,0.0003141742,0.0002972398,0.0001617332,0.00005689809,0.00005069153,0.000253138,0.0001435893,0.00005767516],"category_scores_gemma":[0.00007972917,0.0002658531,0.0001702707,0.000207579,0.00003565606,0.0002582885,0.00004761089,0.0001138182,0.00004058114],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001739971,"about_ca_system_score_gemma":0.00001643408,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001115723,"about_ca_topic_score_gemma":0.000002233279,"domain_scores_codex":[0.9984636,0.00001387619,0.0003756586,0.0002920523,0.0002253405,0.0006295246],"domain_scores_gemma":[0.9991005,0.0003369623,0.00003670762,0.0002756847,0.00006619715,0.0001839459],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000015843,0.00001308128,0.002091512,0.0001311459,0.00006931623,0.000004475929,0.00004126031,0.03231713,0.9532433,0.0001658231,0.0002197878,0.01168734],"study_design_scores_gemma":[0.001180618,0.0001034968,0.002356507,0.0001954983,0.00001989838,0.000007624865,0.0000407528,0.00842609,0.9793474,0.0001563604,0.007320418,0.0008453082],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3203955,0.0002655495,0.6771739,0.00002843289,0.0006605579,0.0003315077,0.0000552294,0.0008753273,0.0002139754],"genre_scores_gemma":[0.9888853,0.00002410245,0.01003189,0.00001018799,0.0006439417,0.0002085971,0.000004737855,0.0001126754,0.00007856551],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6684898,"threshold_uncertainty_score":0.9999794,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0227005244743368,"score_gpt":0.2595221162863683,"score_spread":0.2368215918120316,"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."}}