{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0003394105,0.0002736861,0.0002749553,0.0002415419,0.001919045,0.0002843055,0.0006197421,0.00008565432,0.00007905725],"category_scores_gemma":[0.00001050614,0.0003326257,0.0000555353,0.0008342719,0.0002367126,0.0003239165,0.0004953992,0.0006554846,0.000002664181],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000041789,"about_ca_system_score_gemma":0.0001008435,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002749989,"about_ca_topic_score_gemma":0.00004132117,"domain_scores_codex":[0.9984382,0.000200215,0.0003882224,0.0004395513,0.0002407516,0.0002930217],"domain_scores_gemma":[0.9985319,0.0002595846,0.0001398708,0.0007681304,0.0001651151,0.0001354364],"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":[0.000009597943,0.0001126314,0.001185974,0.00009602013,0.0001275236,5.693734e-7,0.0006837097,0.00263269,0.9557479,0.0003309613,0.00001824716,0.0390542],"study_design_scores_gemma":[0.002818695,0.0003547493,0.01060808,0.0004672572,0.002361803,0.0001647518,0.01395924,0.5739596,0.3213636,0.03106292,0.03949175,0.003387596],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6102197,0.04801167,0.3364891,0.0004085523,0.0001140211,0.001944943,0.0007406118,0.0009796578,0.001091754],"genre_scores_gemma":[0.9902995,0.000508141,0.007395531,0.00005904796,0.00006529244,0.001494318,0.00009274616,0.00005270575,0.00003274076],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6343843,"threshold_uncertainty_score":0.9999126,"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."}}