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Machine Learning–Based Signal Quality Evaluation of Single-Period Radial Artery Pulse Waves: Model Development and Validation

Machine Learning–Based Signal Quality Evaluation of Single-Period Radial Artery Pulse Waves: Model Development and Validation

Wang and Lu [10] utilized a k-nearest neighbor (KNN) classifier based on manual label data to measure the quality of the segmented single periods. However, the details and accuracy of the classifier were not shown. In a recent study, a method based on the Hilbert-Huang transform and an autoregressive moving average model was proposed to remove noise-induced mutations [11]. The accuracy of this method could reach 91.8% in a sample size of 207.

Xiaodong Ding, Feng Cheng, Robert Morris, Cong Chen, Yiqin Wang

JMIR Med Inform 2020;8(6):e18134