{"id":"W2154511562","doi":"10.1109/iembs.2008.4649817","title":"Performance analysis of stationary and discrete wavelet transform for action potential detection from sympathetic nerve recordings in humans","year":2008,"lang":"en","type":"article","venue":"","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Discrete wavelet transform; Wavelet; Wavelet transform; Sympathetic nerve; Continuous wavelet transform; Autonomic nervous system; Neurophysiology; Sympathetic nervous system; Pattern recognition (psychology); Computer science; Blood pressure; Artificial intelligence; Mathematics; Medicine; Neuroscience; Heart rate; Psychology; Internal medicine","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":[],"consensus_categories":[],"category_scores_codex":[0.00005758605,0.00009313662,0.0001668856,0.0002791625,0.00005584309,0.000006812589,0.00003548653,0.00005046197,0.00001364217],"category_scores_gemma":[0.000004286422,0.00009523294,0.00007120783,0.0003051191,0.00002050521,0.0003021011,0.000004858302,0.00006277902,5.413653e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007332756,"about_ca_system_score_gemma":0.00000437051,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003216512,"about_ca_topic_score_gemma":0.000316224,"domain_scores_codex":[0.9994374,0.000007227245,0.0002115035,0.0001304196,0.00009159558,0.0001218325],"domain_scores_gemma":[0.9998074,0.00004228172,0.00002742257,0.00006886041,0.00002701147,0.00002702822],"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":[0.00008023419,0.00002320275,0.03874677,0.0001254951,0.0003820858,0.000003529235,0.001673692,0.05688766,0.833087,0.000005720511,0.000004590007,0.06898007],"study_design_scores_gemma":[0.0005748535,0.0001292873,0.2270589,0.00002776757,0.0002342361,0.000002100744,0.0003962514,0.3246697,0.4465586,0.0001336289,0.00001236995,0.0002022888],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8824039,0.00002622822,0.1171333,0.000004890375,0.0001120378,0.000137591,0.00002627926,0.00004425859,0.0001115175],"genre_scores_gemma":[0.9982508,0.0001727975,0.001429401,0.000001185419,0.00004398566,0.00003054935,0.00003635299,0.00001562652,0.00001929714],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3865283,"threshold_uncertainty_score":0.3883486,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01483841264033448,"score_gpt":0.2207876640225049,"score_spread":0.2059492513821704,"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."}}