An automatic glucose monitoring signal denoising method with noise level estimation and responsive filter updating. (March 2018)

• Record Type: Journal Article
• Authors: Zhao, Hong; Zhao, Chunhui; Gao, Furong
• Is Part Of: Biomedical signal processing and control. Volume 41(2018)
• Page Start: 172
• Page End: 185
• Publication Date: 2018-03
• Subjects: Signal denoising -- Kalman filter -- Expectation maximization (EM) -- Noise variability -- Power spectral density (PSD) -- Type 1 diabetes mellitus (T1DM); Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2017.11.016
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: An automatic glucose monitoring signal denoising method is proposed with noise estimation and responsive filter updating. It can accurately determine proper filter parameters by exploring state transition matrix and noise level. It can automatically detect the noise variability and define a proper confidence interval. The feasibility and performance are illustrated for thirty in silico subjects and ten clinical subjects. 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 internal blood glucose dynamics and the real noise level. Besides, the noise level may change from device to device, subject to subject and also within the subject as time goes on which thus requires that the filter parameters should be adjusted to follow the noise changes. In this paper, we proposed an automatic CGM signal denoising method which covers three important components. First, the state transition matrix which reveals the internal blood glucose dynamics and plays an important role in determining filter parameters can be estimated in response to different patients. Second, the real noise level can be estimated which are used to set the values of filter parameters properly. Third, a responsive filter updating rule is developed which can judge whether the values of filter parameters should be updated in response to the variability of signal-to-noise ratio. The process of dealing with the CGM signals is executing as follows: the model parameters and the noise level are evaluated using Expectation Maximization (EM) algorithm which can fix proper filter parameters for the current signals. Then, 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 and ten clinical subjects. The proposed method can work well to identify the changes of noise level and determine proper filter parameters.