TMN-LAKDE: Characterizing Sleep Instability via Prediction Intervals of Dynamic EEG Spectral Networks

Sleep instability is a typical characteristic of insomnia, manifested as the inability of the brain to maintain a stable state, but its precise quantification is still challenging. We assume that sleep instability fundamentally reflects an increase in unpredictability in the evolution of brain network dynamics. To verify this, an interval prediction framework combining the temporal mobile network (TMN) and local adaptive kernel density estimation (LAKDE) is proposed to characterize the sleep instability. Specifically, TMN predicts future network states, while LAKDE module quantifies the uncertainty of these predictions by generating prediction intervals (PIs). Experiments on SIESTA and Sleep-EDF databases have shown that this method can construct well calibrated PIs. The key finding is that the PI normalized average width (PINAW) of subjects with sleep disorders is significantly higher than that of the healthy control group, validating that wider PIs are a mechanistic biomarker of sleep instability. In addition, this study further revealed a significant correlation between PINAW and traditional indicators such as number of sleep stage transitions, indicating that dynamic instability based on prediction uncertainty shares a common physiological basis with sleep fragmentation phenomena, establishing interval prediction as a paradigm for quantifying sleep instability.