{"id":"W3089350906","doi":"10.1609/aaai.v35i1.16126","title":"CardioGAN: Attentive Generative Adversarial Network with Dual Discriminators for Synthesis of ECG from PPG","year":2021,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":83,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Photoplethysmogram; Computer science; Artificial intelligence; Dual (grammatical number); Generator (circuit theory); Generative adversarial network; Wearable computer; Smartwatch; Pattern recognition (psychology); Computer vision; Deep learning; Power (physics); Embedded system","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0006459062,0.0008571874,0.0004605106,0.000273453,0.0001333652,0.0003073188,0.000784062,0.0006978865,0.001430336],"category_scores_gemma":[0.001643093,0.0002942277,0.0004378821,0.0001781872,0.0004355959,0.0004806888,0.0009077201,0.001098952,0.0004260783],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003965752,"about_ca_system_score_gemma":0.0002609511,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001497733,"about_ca_topic_score_gemma":0.002055603,"domain_scores_codex":[0.9997862,0.00006637337,0.000006775327,0.00006908896,0.00004527704,0.00002624115],"domain_scores_gemma":[0.9995216,0.0003307453,0.00003490441,0.00004690501,0.00004613665,0.00001968163],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003376203,0.0001254235,0.001522217,0.0000993133,0.00010803,0.0002328244,0.00007300742,0.7827194,0.01863263,0.005924516,0.005028105,0.185197],"study_design_scores_gemma":[0.000005107051,0.00002889357,0.0001500591,0.000004279661,0.000006459994,0.0000335863,0.000002425716,0.9963887,0.001703594,0.00134557,0.0003271668,0.000004287709],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03625151,0.0008034495,0.9577745,0.0004163891,0.0001602834,0.00005782178,0.0001772567,0.001494522,0.002864387],"genre_scores_gemma":[0.856189,0.0004426024,0.1332984,0.0007814733,0.0001331416,0.0001260704,0.0007267445,0.000173335,0.008129179],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001497733,"threshold_uncertainty_score":0.004784942,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0417995205250873,"score_gpt":0.2452380388984095,"score_spread":0.2034385183733222,"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."}}