{"id":"W4293863165","doi":"10.1109/siu55565.2022.9864829","title":"Blood Pressure Level and Heart Rate Detection from Photoplethysmography Signals Using DT–CWT","year":2022,"lang":"en","type":"article","venue":"2022 30th Signal Processing and Communications Applications Conference (SIU)","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Stantec (Canada)","funders":"","keywords":"Photoplethysmogram; Kurtosis; Standard deviation; Blood pressure; Support vector machine; Complex wavelet transform; Linear regression; Heart rate; Skewness; Mathematics; Correlation coefficient; Random forest; Wavelet transform; Pattern recognition (psychology); Artificial intelligence; Wavelet; Statistics; Computer science; Medicine; Internal medicine; Discrete wavelet transform; Telecommunications","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":[0.0003808482,0.0004161,0.0003318229,0.000862305,0.00009072433,0.0004000549,0.0002089751,0.0004822669,0.0007723281],"category_scores_gemma":[0.001513623,0.0001083467,0.0002682536,0.0008512792,0.0001181292,0.0004776175,0.0001430349,0.0002163979,0.00040279],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000770138,"about_ca_system_score_gemma":0.0001171018,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004544783,"about_ca_topic_score_gemma":0.0005969808,"domain_scores_codex":[0.9996378,0.00008232587,0.00003200581,0.0001015221,0.0001250976,0.00002108641],"domain_scores_gemma":[0.9996159,0.0001983118,0.00005130268,0.000028668,0.00009339541,0.00001232972],"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":[0.0006940433,0.0001846949,0.02591554,0.0007659289,0.0001685868,0.0004396827,0.0002219321,0.005747075,0.4505191,0.0006810988,0.001200216,0.5134621],"study_design_scores_gemma":[0.0001069846,0.001157039,0.3101118,0.0001363336,0.0005694111,0.005743423,0.0003090295,0.3692455,0.3018511,0.001981963,0.00863168,0.0001558025],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4236265,0.002076597,0.5688839,0.0001508548,0.0001847139,0.0001379786,0.0006034777,0.000699782,0.003636158],"genre_scores_gemma":[0.8119311,0.001577712,0.1834447,0.0001360364,0.0001399211,0.0001582956,0.0006087573,0.00006933854,0.00193402],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.000862305,"threshold_uncertainty_score":0.002583683,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04147150066666915,"score_gpt":0.2581999357047215,"score_spread":0.2167284350380523,"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."}}