An Automatic Denoising Method with Estimation of Noise Level and Detection of Noise Variability in Continuous Glucose Monitoring

Abstract

Although continuous glucose monitoring (CGM) devices have been the crucial part of the artificial pancreas, their success has been discounted by random measurement noise. The difficulty of denoising methods for CGM is that the filter parameters are hard to be determined to well reflect the real noise level. Besides, the noise level may show both intraindividual and interindividual variability which thus requires that the filter parameters should be adjusted to follow the noise changes. In this paper, we proposed an automatic denoising method which covers two important components. On the one hand, the noise level can be estimated so that the filter parameters are determined properly. On the other hand, the variability of signal-to-noise ratio can be detected for self-adjustment of filter parameters. First, the noise level is evaluated using expectation maximization algorithm which can fix proper filter parameters for the current signals. Second, a confidence interval is defined by computing the power spectral density (PSD) of the CGM signals to identify the changes of noise level which can tell whether or not the parameters of Kalman filter (KF) should be adjusted. The above issues are investigated based on thirty in silico subjects. The proposed method can work well to identify the changes of noise level and determine proper filter parameters.