{"id":"W3005055041","doi":"10.1109/taffc.2020.3014842","title":"Self-Supervised ECG Representation Learning for Emotion Recognition","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Affective Computing","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":368,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Artificial intelligence; Pattern recognition (psychology); Transfer of learning; Convolutional neural network; Emotion classification; Multi-task learning; Supervised learning; Machine learning; Transformation (genetics); Feature learning; Task (project management); Deep learning; Artificial neural network; Speech recognition; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002609819,0.0002052782,0.000219725,0.0001745401,0.000474666,0.00004970905,0.00007455819,0.0001601871,0.0003369499],"category_scores_gemma":[0.00005109467,0.0002352036,0.0002312066,0.00041497,0.0000264933,0.0001667169,0.000001340598,0.0004296231,0.0004946138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000923054,"about_ca_system_score_gemma":0.00001850628,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001626115,"about_ca_topic_score_gemma":0.000004830318,"domain_scores_codex":[0.9982175,0.0004673221,0.0002988462,0.0005733246,0.0001586085,0.0002844177],"domain_scores_gemma":[0.9988503,0.0005263792,0.0001554937,0.0001156466,0.000220879,0.000131265],"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":[0.0004159505,0.0004681217,0.0001231777,0.0001030699,0.0002295933,0.000004175592,0.009911316,0.008504879,0.003722382,0.00004052529,0.0003020687,0.9761747],"study_design_scores_gemma":[0.008961821,0.003505127,0.003991163,0.0002331783,0.000443374,0.00005171694,0.008826058,0.9157484,0.0558414,0.0005519614,0.000860152,0.0009857131],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1799847,0.00001020514,0.8137459,0.0004137756,0.001226993,0.0009637867,0.00001478688,0.0006677061,0.002972104],"genre_scores_gemma":[0.9910952,0.000008972779,0.007619774,0.0005987588,0.0003431961,0.00009838488,0.00006867983,0.00005064676,0.0001164137],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.975189,"threshold_uncertainty_score":0.9591323,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06766507257346926,"score_gpt":0.3295969809081446,"score_spread":0.2619319083346753,"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."}}