In this paper, we present a comprehensive approach ranging from optimized data preprocessing to the design of a novel multi-view cross-fusion transformer (MvCFT) network....
Abstract:
Noninvasive blood glucose (BG) measurement could significantly improve the prevention and management of diabetes. In this paper, we present a robust novel paradigm based ...Show MoreMetadata
Abstract:
Noninvasive blood glucose (BG) measurement could significantly improve the prevention and management of diabetes. In this paper, we present a robust novel paradigm based on analyzing photoplethysmogram (PPG) signals. The method includes signal pre-processing optimization and a multi-view cross-fusion transformer (MvCFT) network for non-invasive BG assessment. Specifically, a multi-size weighted fitting (MSWF) time-domain filtering algorithm is proposed to optimally preserve the most authentic morphological features of the original signals. Meanwhile, the spatial position encoding-based kinetics features are reconstructed and embedded as prior knowledge to discern the implicit physiological patterns. In addition, a cross-view feature fusion (CVFF) module is designed to incorporate pairwise mutual information among different views to adequately capture the potential complementary features in physiological sequences. Finally, the subject-wise 5-fold cross-validation is performed on a clinical dataset of 260 subjects. The root mean square error (RMSE) and mean absolute error (MAE) of BG measurements are 1.129 mmol/L and 0.659 mmol/L, respectively, and the optimal Zone A in the Clark error grid, representing none clinical risk, is 87.89%. The results indicate that the proposed method has great potential for homecare applications.
In this paper, we present a comprehensive approach ranging from optimized data preprocessing to the design of a novel multi-view cross-fusion transformer (MvCFT) network....
Published in: IEEE Journal of Biomedical and Health Informatics ( Volume: 28, Issue: 4, April 2024)
Funding Agency:
Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing, China
Endocrinology Department, The Ninth People's Hospital, Chongqing, China
School of Natural Medicine, University of the Western Cape, Bellville, South Africa
Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing, China
Department of Electronic Engineering, Chinese University of Hong Kong, Shatin, China
Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing, China
Department of Electrical and Computer Engineering, University of Alabama in Huntsville, Huntsville, AL, USA
Contents Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing, China
Endocrinology Department, The Ninth People's Hospital, Chongqing, China
School of Natural Medicine, University of the Western Cape, Bellville, South Africa
Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing, China
Department of Electronic Engineering, Chinese University of Hong Kong, Shatin, China
Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing, China
Department of Electrical and Computer Engineering, University of Alabama in Huntsville, Huntsville, AL, USA
