{"id":"W2973847306","doi":"10.3390/s19183997","title":"Low Resource Complexity R-peak Detection Based on Triangle Template Matching and Moving Average Filter","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; Grand Challenges Canada","keywords":"Robustness (evolution); Computer science; Wearable computer; Wearable technology; Computational complexity theory; Filter (signal processing); Artificial intelligence; Matching (statistics); Noise (video); Algorithm; Pattern recognition (psychology); Computer vision; Real-time computing; Mathematics; Embedded system; Statistics","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":[],"consensus_categories":[],"category_scores_codex":[0.00022529,0.0001102176,0.0002339229,0.0001343046,0.00009845429,0.00002670826,0.00002802064,0.00006029279,0.00008071676],"category_scores_gemma":[0.00005313271,0.00009483236,0.00008691475,0.0001197337,0.00002239478,0.00002654948,0.00001396698,0.0002006893,0.00007910177],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000444021,"about_ca_system_score_gemma":0.000007731622,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001294899,"about_ca_topic_score_gemma":0.00001162558,"domain_scores_codex":[0.9992102,0.00007068158,0.0001454967,0.000232194,0.0001771926,0.0001642234],"domain_scores_gemma":[0.9994763,0.0001235205,0.00005698015,0.0002393431,0.00002140409,0.00008251633],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003971921,0.000759988,0.147454,0.00180003,0.0006633997,0.00056257,0.004452005,0.1097243,0.5897344,0.00007213031,0.0005101802,0.140295],"study_design_scores_gemma":[0.008362256,0.001134776,0.1356871,0.001675055,0.0003462625,0.00008696382,0.001532919,0.6641648,0.1790552,0.0002814702,0.006858895,0.0008142722],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9961287,0.00001023739,0.0002827035,0.0002672448,0.0001023857,0.000117929,0.000003513459,0.00007937542,0.003007864],"genre_scores_gemma":[0.9962116,0.000001940183,0.0004081883,0.0002199087,0.0001749436,0.000001415286,0.000008570356,0.00001932699,0.002954127],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5544404,"threshold_uncertainty_score":0.3867151,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01924021344822323,"score_gpt":0.25013329396848,"score_spread":0.2308930805202568,"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."}}