{"id":"W2233780080","doi":"10.1186/s12938-016-0122-0","title":"Robust detection of heartbeats using association models from blood pressure and EEG signals","year":2016,"lang":"en","type":"article","venue":"BioMedical Engineering OnLine","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Institute for Information and Communications Technology Promotion; National Research Foundation of Korea; Gwangju Institute of Science and Technology; Ministry of Science, ICT and Future Planning; National Research Foundation","keywords":"Electroencephalography; Artificial intelligence; Pattern recognition (psychology); Computer science; Association (psychology); Biomedical engineering; Speech recognition; Medicine; Neuroscience; Psychology","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":[0.002216208,0.001549518,0.001246634,0.001356947,0.0003470724,0.001333452,0.001018186,0.001130841,0.001390016],"category_scores_gemma":[0.005020717,0.0003857526,0.001514584,0.001071178,0.0003408017,0.001485206,0.001386826,0.001884885,0.001654097],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003962132,"about_ca_system_score_gemma":0.000843313,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002491934,"about_ca_topic_score_gemma":0.00209153,"domain_scores_codex":[0.9985681,0.0002283226,0.0001031878,0.0004996922,0.0004631635,0.0001374985],"domain_scores_gemma":[0.9982117,0.0006733827,0.0002668353,0.0002146128,0.000528597,0.0001049018],"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":[0.0007021936,0.0005543901,0.01372903,0.0002220931,0.0005317556,0.0002467279,0.0001635998,0.1716694,0.02790339,0.001642897,0.00341115,0.7792233],"study_design_scores_gemma":[0.00001085119,0.0001093565,0.003587269,0.00001840304,0.00006438605,0.0001326276,0.00002569729,0.9874277,0.005937493,0.001634829,0.001029672,0.00002169852],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04578757,0.001068981,0.9491337,0.0002372593,0.0001504004,0.00007513708,0.0002211527,0.001867227,0.001458515],"genre_scores_gemma":[0.7584703,0.001103754,0.2342504,0.0002840349,0.0002834043,0.0001419224,0.001410074,0.0001895402,0.003866617],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002491934,"threshold_uncertainty_score":0.01172054,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02450445306497518,"score_gpt":0.2428749695540772,"score_spread":0.218370516489102,"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."}}