基于自适应多任务学习的睡眠生理时序分类方法  

Sleep physiological time series classification method based on adaptive multi-task learning

作　　者：宋钰丹 王晶[1,2,3] 王雪徽 马朝阳 林友芳 SONG Yudan;WANG Jing;WANG Xuehui;MA Zhaoyang;LIN Youfang(School of Computer and Information Technology,Beijing Jiaotong University,Beijing 100044,China;Beijing Key Lab of Traffic Data Analysis and Mining,Beijing Jiaotong University,Beijing 100044,China;Key Laboratory of Intelligent Application Technology for Civil Aviation Passenger Services,Beijing 101318,China)

机构地区：[1]北京交通大学计算机与信息技术学院,北京100044 [2]北京交通大学交通数据分析与挖掘北京市重点实验室,北京100044 [3]民航旅客服务智能化应用技术重点实验室,北京101318

出　　处：《计算机应用》2024年第2期654-662,共9页journal of Computer Applications

基　　金：中央高校基本科研业务费资助项目(2021JBM007)。

摘　　要：针对睡眠阶段与睡眠呼吸暂停低通气之间相关性的问题,提出一种基于自适应多任务学习的睡眠生理时序分类方法。该方法利用单导脑电与心电检测睡眠分期和睡眠呼吸暂停低通气综合征(SAHS),构造双流时间依赖学习模块,在两个任务的联合监督下提取共享特征,设计自适应任务间关联性学习模块,利用通道注意力机制建模睡眠阶段和呼吸暂停低通气之间的相关性。在两个公开数据集上的实验结果表明,所提方法可以同时完成睡眠分期与SAHS检测。在UCD数据集上,所提方法睡眠分期准确率、宏F1分数(MF1)、受试者特性曲线下面积(AUC)与TinySleepNet相比分别提升了1.21个百分点、1.22个百分点和0.0083,SAHS检测的宏F2分数(MF2)、受试者特性曲线下面积、召回率与6-layer CNN模型相比,分别提升了11.08个百分点、0.0537和15.75个百分点,能检出更多患病片段。所提方法可应用于家庭睡眠监测或移动医疗中,实现高效、便捷的睡眠质量评估,辅助医生对SAHS进行初步诊断。Aiming at the correlation problem between sleep stages and sleep apnea hypopnea,a sleep physiological time series classification method based on adaptive multi-task learning was proposed.Single-channel electroencephalogram and electrocardiogram were used for sleep staging and Sleep Apnea Hypopnea Syndrome(SAHS)detection.A two-stream time dependence learning module was utilized to extract shared features under joint supervision of the two tasks.The correlation between sleep stages and sleep apnea hypopnea was modeled by the adaptive inter-task correlation learning module with channel attention mechanism.The experimental results on two public datasets indicate that the proposed method can complete sleep staging and SAHS detection simultaneously.On UCD dataset,the accuracy,MF1(Macro F1-score),and Area Under the receiver characteristic Curve(AUC)for sleep staging of the proposed method were 1.21 percentage points,1.22 percentage points,and 0.0083 higher than those of TinySleepNet;its MF2(Macro F2-score),AUC,and recall of SAHS detection were 11.08 percentage points,0.0537,and 15.75 percentage points higher than those of the 6-layer CNN model,which meant more disease segments could be detected.The proposed method could be applied to home sleep monitoring or mobile medical to achieve efficient and convenient sleep quality assessment,assisting doctors in preliminary diagnosis of SAHS.

关 键 词：睡眠分期 睡眠呼吸暂停低通气检测 脑电图 心电图 深度学习 多任务学习 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]