{"id":"W2166016327","doi":"10.1186/1475-925x-11-19","title":"Transfer Entropy Estimation and Directional Coupling Change Detection in Biomedical Time Series","year":2012,"lang":"en","type":"article","venue":"BioMedical Engineering OnLine","topic":"Heart Rate Variability and Autonomic Control","field":"Medicine","cited_by":131,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; National Heart, Lung, and Blood Institute; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health; Thoracic Society of Australia and New Zealand; American Heart Association","keywords":"Outlier; Sample entropy; Transfer entropy; Mathematics; Kernel density estimation; Entropy (arrow of time); Domperidone; Biological system; Statistics; Algorithm; Pattern recognition (psychology); Computer science; Time series; Artificial intelligence; Physics; Estimator; Principle of maximum entropy; Thermodynamics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002132328,0.0006293796,0.0004058141,0.001569273,0.000244678,0.0006253345,0.0003668483,0.0005725455,0.0007328689],"category_scores_gemma":[0.01060008,0.0001728805,0.0005428345,0.0007603829,0.0005818734,0.0008230727,0.0006734219,0.0005549742,0.0001293987],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003822449,"about_ca_system_score_gemma":0.0003808734,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001041325,"about_ca_topic_score_gemma":0.0007873838,"domain_scores_codex":[0.999406,0.0002075546,0.0000495657,0.0001374128,0.0001542421,0.00004517366],"domain_scores_gemma":[0.995582,0.003406952,0.0004492815,0.0001964172,0.0002843844,0.00008094402],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001392001,0.0003181909,0.04073871,0.0005970687,0.0005974031,0.0004658474,0.0005886087,0.4235415,0.1101683,0.00651867,0.0009698812,0.4141039],"study_design_scores_gemma":[0.00001862734,0.0002388755,0.03783635,0.00003250345,0.00006387391,0.0002757841,0.00007047739,0.9339108,0.02187778,0.004985942,0.0006320478,0.00005684889],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3310109,0.0008032308,0.6662042,0.0001924691,0.00006543879,0.00006712473,0.0001814727,0.0003875021,0.001087676],"genre_scores_gemma":[0.9213709,0.0002642568,0.07751536,0.00004610253,0.00004528565,0.00005633558,0.0002293129,0.00004899275,0.0004234653],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002132328,"threshold_uncertainty_score":0.01127696,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01304841360711123,"score_gpt":0.2409736700840446,"score_spread":0.2279252564769334,"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."}}