{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.002574925,0.001720523,0.008618344,0.0009438309,0.0002698342,0.0004466106,0.001123747,0.000719375,0.0001988151],"category_scores_gemma":[0.0004388216,0.001514064,0.001043936,0.0006319389,0.0003254315,0.000275499,0.001055358,0.0005639016,0.00004905297],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002809466,"about_ca_system_score_gemma":0.0003896671,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001759452,"about_ca_topic_score_gemma":0.000002095565,"domain_scores_codex":[0.9903373,0.001120464,0.004507354,0.002264363,0.0007104352,0.00106015],"domain_scores_gemma":[0.9926171,0.001018342,0.003464179,0.001430941,0.001054035,0.0004154255],"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":[0.0001077029,0.0002606051,0.000004741688,0.3111598,0.001000871,0.00001127177,0.0001513399,3.212402e-7,0.0005013515,0.0001940418,0.001817083,0.6847909],"study_design_scores_gemma":[0.001369188,0.0004119273,0.00001786399,0.2530635,0.003763857,0.00006252496,0.00005260307,0.00003086793,0.0004706567,0.00006432747,0.7397761,0.0009165477],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00008793197,0.9494414,0.03525201,0.0004919485,0.006245123,0.007677214,0.0003765639,0.0003925159,0.0000352994],"genre_scores_gemma":[0.0001167483,0.9753113,0.01347794,0.0002371024,0.005220623,0.002538546,0.002715155,0.000198575,0.0001840234],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.7379591,"threshold_uncertainty_score":0.9995541,"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."}}