基于多任务注意力网络的非接触式睡眠监测  

Multi-Task Attention Network for Contactless Sleep Monitoring

作　　者：李思恒 金蓓弘[1,2,3] 张扶桑 王志[1,2,3] 马俊麒 苏畅 任晓勇[4] 刘海琴 Li Siheng;Jin Beihong;Zhang Fusang;Wang Zhi;Ma Junqi;Su Chang;Ren Xiaoyong;and Liu Haiqin(Technology Center of Software Engineering,Institute of Software,Chinese Academy of Sciences,Beijing 100190;Key Laboratory of System Software(Chinese Academy of Sciences)and State Key Laboratory of Computer Science(Institute of Software,Chinese Academy of Sciences),Beijing 100190;University of Chinese Academy of Sciences,Beijing 100049;The Second Affiliated Hospital of Xi’an Jiaotong University,Xi’an 710000)

机构地区：[1]中国科学院软件研究所软件工程技术中心,北京100190 [2]基础软件与系统重点实验室(中国科学院)、计算机科学国家重点实验室(中国科学院软件研究所),北京100190 [3]中国科学院大学,北京100049 [4]西安交通大学第二附属医院,西安710000

出　　处：《计算机研究与发展》2024年第11期3739-3753,共15页Journal of Computer Research and Development

基　　金：国家自然科学基金项目(62072450,62172394);中国科学院软件研究所基础研究项目(ISCAS-JCMS-202305)。

摘　　要：睡眠几乎占据了一个人每天三分之一的时间,它与人体的健康状况紧密相关.由于睡眠过程中各睡眠阶段的持续时间和转换情况直接影响人的睡眠质量,因此识别睡眠阶段是睡眠监测最基本和最重要的任务.然而,睡眠中出现的睡眠障碍会导致睡眠结构变得复杂,这增加了睡眠阶段分类的难度.已有的非接触式睡眠阶段分类工作大多对睡眠结构的复杂性认识不足,忽视了睡眠阶段和睡眠障碍之间的联系.因此,这些工作难以在睡眠障碍患者上取得较好的性能.提出一种非接触式睡眠监测系统,利用超宽带(ultra-wideband,UWB)信号来识别人体睡眠阶段的变化情况.该系统包含了一个序列预测模型,使用一个基于注意力机制的序列编码器挖掘不同睡眠阶段之间的时序转换关系,并通过一个对比学习模块提高编码器的泛化性.值得一提的是,该序列预测模型采用了一个基于多任务学习的两阶段训练框架,并在模型的微调阶段通过多专家学习模块将睡眠障碍信息融入模型中,从而降低了睡眠障碍对睡眠阶段预测造成的干扰.在110名受试者(包括健康个体和不同程度睡眠障碍患者)中进行实验评估,实验结果表明所提出的模型的性能优于基线方法.Sleep takes up nearly one-third of a person’s day and is closely related to human health.Since the durations and transitions over different sleep stages during sleep directly affect a person’s sleep quality,identifying sleep stages has become the most basic and important task in sleep monitoring.However,sleep disorders occurring in sleep can lead to complex sleep structures,thereby increasing the difficulty of classifying sleep stages.Most of the existing contactless solutions for sleep stage classification lack a sufficient understanding of the complexity in sleep structure,ignoring the relationship between sleep stage and sleep disorder.Therefore,these solutions fail to achieve great performance in patients with sleep disorders.In this paper,we propose a sleep monitoring system that focuses on predicting sleep stages from ultra-wideband(UWB)signals.We design a sequence prediction model that combines an attention-based sequence encoder and a contrastive learning module to extract the temporal progression of sleep and improve the generalizability of the encoder.Particularly,the key to our approach is a multi-task fine-tuning strategy that incorporates sleep disorder information into sleep staging to reduce the interference of sleep disorders with sleep stage prediction.We conduct extensive experiments on 110 subjects,including healthy individuals and patients with different severities of sleep disorders.The experimental results demonstrate that the performance of our model is superior to the baseline methods.

关 键 词：非接触式感知 超宽带 深度神经网络 多任务学习 睡眠分期 睡眠障碍 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]