Time Frequency Analysis-Based Averaging and Fusion of Features for Wearable Non-Invasive Blood Glucose Estimation

Abstract

This paper proposes a Clark error grid based stacking method for fusing various time frequency averaged features together for reducing the measurement errors for performing the wearable non-invasive blood glucose estimation. Like the conventional time frequency representation based denoising methods, the most critical issue is on the selection of the time frequency components. In this paper, three time frequency representation based averaging methods are investigated. They are the discrete cosine transform (DCT) based averaging method, the singular spectrum analysis (SSA) based averaging method and the empirical mode decomposition (EMD) based averaging method. It is found that retaining 40% of the DCT coefficients as well as three SSA components and three intrinsic mode functions (IMFs) could yield the best blood glucose estimation performances. Two datasets are employed for demonstrating the effectiveness and the robustness of our proposed method. The most common blood glucose estimation method and our proposed method can yield the Pearson’s correlation coefficient (R) at 0.4308 and 0.9172, respectively, the mean absolute error (MAE) at 0.8783 and 0.8193, respectively, the root mean squares error (RMSE) at 1.0680 and 0.9621, respectively, and the mean absolute relative difference (MARD) at 0.1504 and 0.1133, respectively, as well as the percentage of the data falling in the zone A of the Clark error grid at 71.1111% and 90.5882%, respectively, for the data in the first dataset. The results obtained for the data in the second dataset are similar. Obviously, our proposed method significantly outperforms the most common blood glucose estimation methods.