{"id":"W7015289913","doi":"","title":"Signal processing and biomedical analysis of data collected from wearable devices in order to evaluate individualized circadian rhythm and reliably extract cardiac vital signs from ambulatory electrocardiograms","year":2020,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Heart Rate Variability and Autonomic Control","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Wearable computer; Ambulatory; Wearable technology; Circadian rhythm; Signal processing; Vital signs; SIGNAL (programming language)","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":[0.000488541,0.0003082229,0.0002687038,0.0006666483,0.0001421574,0.0007008884,0.000154738,0.0003251608,0.002629927],"category_scores_gemma":[0.001178927,0.00009865363,0.0002953975,0.0007570888,0.0001801054,0.0003322541,0.0002256976,0.0004184842,0.001535688],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001128109,"about_ca_system_score_gemma":0.0003438136,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003915422,"about_ca_topic_score_gemma":0.0006258264,"domain_scores_codex":[0.9997433,0.0000547732,0.00001727987,0.00005809557,0.0001081367,0.00001851375],"domain_scores_gemma":[0.9996952,0.0001447763,0.00002566802,0.00002960813,0.00008883513,0.00001607704],"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":[0.0001902636,0.0001850225,0.004546694,0.000701478,0.0001183094,0.0001716174,0.0003587454,0.004877172,0.1801562,0.003169095,0.01132439,0.7942011],"study_design_scores_gemma":[0.000180461,0.002943276,0.3165967,0.0009079452,0.0004394168,0.002140149,0.001415735,0.179638,0.269211,0.02171732,0.2045151,0.000294905],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2771544,0.02264077,0.6605707,0.003239648,0.002264309,0.0004318171,0.002499808,0.001474298,0.0297243],"genre_scores_gemma":[0.6453272,0.01948397,0.2979123,0.0006966043,0.001291437,0.0005151219,0.002966298,0.0003172327,0.0314899],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002629927,"threshold_uncertainty_score":0.008798003,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02595013057397354,"score_gpt":0.2861011978400563,"score_spread":0.2601510672660828,"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."}}