%0 Journal Article
%T 用于实时睡眠分期的智能设备设计与实现<br>Design and Implementation of a Smart Device for Real-Time Sleep Staging
%A 高中翔
%A 何宏
%A 汪焰兵
%A 赵颖
%J Modeling and Simulation
%P 826-837
%@ 2324-870X
%D 2025
%I Hans Publishing
%R 10.12677/mos.2025.144333
%X 本文针对具有睡眠结构紊乱和睡眠障碍等睡眠问题的人群需要实时获取睡眠状态的问题&#65292;开发了一种能够采集双通道脑电信号并进行实时睡眠分期的智能设备。基于ADS1299模数转换器&#65292;采集两个通道的脑电信号&#65292;并在微处理器中进行实时的信号分割、滤波和离散小波变换等预处理。系统提取时域和频域特征&#65292;并利用支持向量机进行睡眠状态识别。采集的数据及分期结果可存储于板载SD卡中&#65292;或通过低功耗蓝牙模块实时传输至外部设备进行查看和分析。经测试&#65292;该设备能够高质量采集双通道脑电信号&#65292;睡眠状态识别准确率达86.4%。测试结果表明该设备在睡眠监测中具备准确性和实用性&#65292;能够为后续家庭和临床睡眠监测提供坚实的基础。<br>In this paper, aiming at the problem that people with sleep problems such as sleep structure disorders and sleep disorders need to obtain real-time sleep states, an intelligent device capable of collecting dual-channel electroencephalogram (EEG) signals and performing real-time sleep staging is developed. Based on the ADS1299 analog-to-digital converter, the EEG signals of two channels are collected, and preprocessing operations such as real-time signal segmentation, filtering, and discrete wavelet transform are carried out in the microprocessor. The system extracts time-domain and frequency-domain features and uses the Support Vector Machine (SVM) to identify sleep states. The collected data and sleep staging results can be stored in the on-board SD card or transmitted in real-time to external devices via the low-power Bluetooth module for viewing and analysis. Through testing, this device can collect dual-channel EEG signals with high quality, and the accuracy of sleep state recognition reaches 86.4%. The test results show that this device is accurate and practical in sleep monitoring, providing a solid foundation for subsequent home and clinical sleep monitoring.
%K 双通道&#65292
%K 脑电信号&#65292
%K 实时&#65292
%K 睡眠分期&#65292
%K 支持向量机<br>Dual-Channel
%K Electroencephalogram
%K Real Time
%K Sleep Staging
%K Support Vector Machine
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=112823