{"id":"W4410131778","doi":"10.32604/cmc.2025.063643","title":"A Review of Deep Learning for Biomedical Signals: Current Applications, Advancements, Future Prospects, Interpretation, and Challenges","year":2025,"lang":"en","type":"review","venue":"Computers, materials & continua/Computers, materials & continua (Print)","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Current (fluid); Interpretation (philosophy); Computer science; Data science; Engineering ethics; Engineering; Electrical engineering","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":[0.00196814,0.001118368,0.001049622,0.00253512,0.0002942793,0.001871965,0.001149476,0.001319537,0.004502287],"category_scores_gemma":[0.004972486,0.0005737322,0.000696755,0.003162781,0.000721084,0.002695831,0.00112655,0.001914962,0.002603731],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007995032,"about_ca_system_score_gemma":0.001691897,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001494951,"about_ca_topic_score_gemma":0.001705876,"domain_scores_codex":[0.9992315,0.0001617917,0.0001144421,0.0001285104,0.000320132,0.00004362039],"domain_scores_gemma":[0.9972881,0.001809988,0.0001396309,0.0001035848,0.0005891785,0.00006952438],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000645922,0.00004967706,0.0005651124,0.01030407,0.0001070538,0.0001075632,0.00009981212,0.002623494,0.00207436,0.01497012,0.03405932,0.9349748],"study_design_scores_gemma":[0.00001899885,0.0002481725,0.001564724,0.00883279,0.000214549,0.0009568264,0.0001459897,0.009705267,0.003402987,0.02558756,0.949227,0.0000950884],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0009691416,0.961998,0.02885285,0.003035445,0.0007479521,0.00002891004,0.0002094968,0.0002050814,0.003953208],"genre_scores_gemma":[0.007443858,0.9739058,0.01299578,0.00194612,0.001257665,0.00004872396,0.0003349624,0.00006930323,0.001997725],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.004502287,"threshold_uncertainty_score":0.01506168,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02313516825356977,"score_gpt":0.3309866983757902,"score_spread":0.3078515301222204,"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."}}