Design and Application of a Portable Sleep Inertia Detection System Based on EEG Signals

Sleep inertia is a transitional state from sleep to wakefulness, accompanied by groggy feelings and cognitive impairment. Previous research on sleep inertia mainly used expensive and cumbersome equipment, and the analysis of physiological signals relied on computers. This work introduces a sleep inertia detection system that consists of a wearable low-power electroencephalogram (EEG) acquisition module based on STM32WB55 and ADS1299, and a data processing module based on the Xilinx^® Zynq^®-7000 XC7Z020. This work recorded the EEG signals of ten subjects in the alert and sleep inertia states to extract the delta power, alpha power, beta power, EEG vigilance, and sample entropy. A linear support vector machine (SVM) was then used to classify the two states based on all subjects’ EEG signals, with an accuracy of 72.5%, and the average accuracy based on a single participant was 8S.9%. Finally, the feature extraction algorithm and SVM parameters were entered into the Zynq^® system-on-chip (SoC) development board to realize onboard processing of the algorithm. The system is capable of evaluating the severity of human sleep inertia, which has reference significance for the practical application of sleep inertia detection.