{"id":"W4392128090","doi":"10.1016/j.compbiomed.2024.108190","title":"Comparative study of respiratory sounds classification methods based on cepstral analysis and artificial neural networks","year":2024,"lang":"en","type":"article","venue":"Computers in Biology and Medicine","topic":"Phonocardiography and Auscultation Techniques","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Rimouski","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mel-frequency cepstrum; Computer science; Artificial neural network; Artificial intelligence; Multilayer perceptron; Pattern recognition (psychology); Feature extraction; Respiratory sounds; Speech recognition; Cepstrum; Support vector machine; Perceptron; Machine learning; Asthma","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001758122,0.0005459847,0.0005089801,0.001446046,0.000266931,0.0007504249,0.0003672139,0.0005786358,0.001216755],"category_scores_gemma":[0.004951149,0.0001377001,0.0004831223,0.0007628495,0.0001852798,0.001099012,0.0002344993,0.0004123291,0.0003853031],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003101921,"about_ca_system_score_gemma":0.0004135187,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004209895,"about_ca_topic_score_gemma":0.003420862,"domain_scores_codex":[0.999065,0.0002435327,0.00008502314,0.0001445279,0.0003892032,0.00007275133],"domain_scores_gemma":[0.9967385,0.002007841,0.0001146418,0.0000970521,0.000986516,0.00005541127],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00206466,0.0003717281,0.01215066,0.0005316907,0.0003360097,0.0001746297,0.0002755629,0.02507084,0.04466303,0.001167467,0.001979216,0.9112145],"study_design_scores_gemma":[0.0001132613,0.001096211,0.07581173,0.0001088843,0.000620019,0.0004908912,0.0004791569,0.8804583,0.03653079,0.0006487601,0.003533646,0.0001083794],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6942391,0.008258181,0.2894517,0.0004575449,0.0005538213,0.0001368442,0.0004131063,0.0007330548,0.005756725],"genre_scores_gemma":[0.9355582,0.002302755,0.05936484,0.0000559321,0.0001576131,0.00005646968,0.0005568338,0.00006562228,0.001881716],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004209895,"threshold_uncertainty_score":0.009297967,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08963061086171427,"score_gpt":0.4524260629998968,"score_spread":0.3627954521381825,"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."}}