Blood Glucose Forecasting Via Fusing Intra- and Inter-Variable Variations

Blood glucose (BG) forecasting aims to help people with Type-1 diabetes (T1D) avoid hyperglycemia or hypoglycemia, which plays a crucial role in medical monitoring. Despite the advancements in deep learning methods for BG forecasting, their ability to predict long-term time series remains limited, and they cannot fully meet the demand for BG forecasting. This limitation stems from the failure to account for both intra- and inter-variable variations simultaneously. To address this challenge, we introduce the Fi² VBlock, which exploits the frequency perspective to fuse intra- and intervariable variations. After transforming to the frequency domain using the Frequency Transform Module, the Frequency Cross Attention between the real and imaginary parts is designed to obtain enhanced frequency representations and capture intravariable variations. In addition, inception blocks are employed to integrate information, thus capturing correlations across different variables. Our backbone network, Fi² V, employs a residual architecture by concatenating multiple Fi² VBlocks, thereby avoiding degradation problems. Experimental evaluations reveal that Fi² V outperforms other baselines on the T1DMS and Dnurse datasets and demonstrates zero-shot generalization across patients.