基于单通道脑电信号的轻量级睡眠分析系统

Lightweight Sleep Analysis System Based on Single-channel EEG Signals

在传统睡眠分期模型中,由于计算资源要求高,难以部署到有限计算能力的设备上.本文开发了一个基于单通道脑电信号的轻量级睡眠分析系统,该系统部署了GhostNet优化的GhostSleepNet神经网络模型,实现了睡眠分期和睡眠质量评估的功能,用户只需要使用脑环并连接至本系统即可在家庭环境下实现准确度高的睡眠分期.其中,卷积神经网络(convolutional neural network,CNN)负责提取高阶特征,GhostNet旨在保持CNN提取特征的准确性的同时,减少模型参数以提高模型的计算效率,门控循环单元(gated recurrent unit,GRU)则专注于捕捉睡眠数据的长期依赖关系与周期性变化.本文对Sleep-EDF数据集的五分类任务进行验证,GhostSleepNet的睡眠分期准确率达到84.17%,比传统睡眠分期模型低3%–5%,但FLOPs仅为5041111040,计算复杂度下降20%–45%,有助于移动设备睡眠分期功能的发展.

Traditional sleep staging models are difficult to deploy in devices with limited computing power due to high requirements of computational resources.In this study,a lightweight sleep analysis system based on single-channel EEG signals is developed,which deploys a GhostNet-optimized neural network model named GhostSleepNet to assess sleep staging and sleep quality.Users only need to use a brain loop and connect it to this system to achieve sleep staging with high accuracy in a home environment.In this system,convolutional neural networks(CNN)are responsible for extracting higher-order features,GhostNet is designed to maintain the accuracy of CNN extracted features while reducing the parameters of the model to improve the computational efficiency,and gated recurrent unit(GRU)focuses on capturing long-term dependencies and cyclic changes in sleep data.Verification of the five classification tasks on the Sleep-EDF dataset shows that the sleep staging accuracy of GhostSleepNet reaches 84.17%,which is 3%-5% lower than that of traditional sleep staging models.However,the number of FLOPs is only 5041111040,and the computational complexity decreases by 20%-45%,contributing to the development of sleep staging for mobile devices.