{"id":"W4416266498","doi":"10.1016/j.bspc.2025.109071","title":"Towards mapping low-cost BCG to ECG using deep learning","year":2025,"lang":"en","type":"article","venue":"Biomedical Signal Processing and Control","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Mastercard Foundation; Carnegie Mellon University","keywords":"Deep learning; Key (lock); Convolutional neural network; Ground truth; Correlation; Artificial neural network; Mean squared error; Correlation coefficient","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004717791,0.0008170935,0.0006313972,0.0003822444,0.0001910834,0.0007828499,0.0008837993,0.0009692098,0.002541086],"category_scores_gemma":[0.002149212,0.0004419968,0.0004693247,0.0005990333,0.0003054051,0.0006781714,0.001080106,0.001348302,0.001277889],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003140365,"about_ca_system_score_gemma":0.000746534,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004970077,"about_ca_topic_score_gemma":0.00828416,"domain_scores_codex":[0.9998316,0.00003381572,0.000008879544,0.00004289433,0.00005704838,0.00002574137],"domain_scores_gemma":[0.9995033,0.0002562994,0.00003966617,0.00007272579,0.0000988209,0.00002923074],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002046891,0.000142719,0.001390602,0.0001884006,0.00009123082,0.0001447453,0.00007404955,0.271018,0.04401092,0.00755291,0.005103894,0.6700777],"study_design_scores_gemma":[0.000004898243,0.00002752325,0.0003344985,0.00001136566,0.000007181114,0.00004413051,0.000006186163,0.9910256,0.004163209,0.003417442,0.0009530394,0.000004761724],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008169535,0.0002629522,0.9897993,0.0001384801,0.00004838445,0.0000165093,0.00006939662,0.0006678236,0.0008277249],"genre_scores_gemma":[0.4669389,0.000636093,0.520796,0.0004640528,0.0001382595,0.00008833205,0.00054175,0.000238411,0.01015815],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004970077,"threshold_uncertainty_score":0.009882271,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01327302442100376,"score_gpt":0.2889853337105556,"score_spread":0.2757123092895519,"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."}}