{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007595624,0.0008317497,0.001053052,0.001310829,0.0003440255,0.001101217,0.001629329,0.00102246,0.002015925],"category_scores_gemma":[0.003142304,0.000330892,0.0007189243,0.001694026,0.0003141964,0.001371372,0.0006989651,0.0006851125,0.0016966],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003146218,"about_ca_system_score_gemma":0.0006925503,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001582193,"about_ca_topic_score_gemma":0.001448737,"domain_scores_codex":[0.9990845,0.0001170421,0.00007188795,0.0002513842,0.0004020531,0.00007310753],"domain_scores_gemma":[0.9990991,0.0003472261,0.00009812408,0.0001229703,0.0002943421,0.00003824508],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005936594,0.0001260098,0.00163894,0.0002513623,0.0001118506,0.0003132584,0.00009450165,0.01408957,0.1675692,0.004493068,0.002509324,0.8082093],"study_design_scores_gemma":[0.00007152536,0.0005096808,0.004145397,0.00002797948,0.0001261533,0.002166814,0.00005152824,0.8038516,0.1785041,0.002697671,0.007748459,0.0000990742],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006062304,0.0002361163,0.9921876,0.00003459171,0.00004644359,0.00004500046,0.00003828676,0.0008317932,0.0005177296],"genre_scores_gemma":[0.143194,0.0004310545,0.8534763,0.0001227788,0.00008984398,0.0001273762,0.0002744696,0.0001762552,0.002107819],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002015925,"threshold_uncertainty_score":0.006743908,"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."}}