Population-Specific Glucose Prediction in Diabetes Care With Transformer-Based Deep Learning on the Edge

作者：Taiyu Zhu, Lei Kuang, Chengzhe Piao, Junming Zeng, Kezhi Li, Pantelis Georgiou · 发表于：IEEE Transactions on Biomedical Circuits and Systems · 年份：2024 · DOI：10.1109/tbcas.2023.3348844 · 被引用次数：62 · 研究领域：Diabetes Management and Research、ECG Monitoring and Analysis、Heart Rate Variability and Autonomic Control

Leveraging continuous glucose monitoring (CGM) systems, real-time blood glucose (BG) forecasting is essential for proactive interventions, playing a crucial role in enhancing the management of type 1 diabetes (T1D) and type 2 diabetes (T2D). However, developing a model generalized to a population and subsequently embedding it within a microchip of a wearable device presents significant technical challenges. Furthermore, the domain of BG prediction in T2D remains under-explored in the literature. In light of this, we propose a population-specific BG prediction model, leveraging the capabilities of the temporal fusion Transformer (TFT) to adjust predictions based on personal demographic data. Then the trained model is embedded within a system-on-chip, integral to our low-power and low-cost customized wearable device. This device seamlessly communicates with CGM systems through Bluetooth and provides timely BG predictions using edge computing. When evaluated on two publicly available clinical datasets with a total of 124 participants with T1D or T2D, the embedded TFT model consistently demonstrated superior performance, achieving the lowest prediction errors when compared with a range of machine learning baseline methods. Executing the TFT model on our wearable device requires minimal memory and power consumption, enabling continuous decision support for more than 51 days on a single Li-Poly battery charge. These findings demonstrate the significant potential of the proposed TFT...