混合智能在精准睡眠阶段判别中的应用研究
Application of intelligent hybrid systems in precise sleep stage discrimination
邵益梓 1黄柏恺 1杜利东 2王鹏 2李振锋 2陈贤祥 2方震2
作者信息
- 1. 中国科学院空天信息创新研究院,北京 100190;中国科学院大学 电子电气与通信工程学院,北京 100049
- 2. 中国科学院空天信息创新研究院,北京 100190;中国科学院大学 电子电气与通信工程学院,北京 100049;中国医学科学院 "个性化呼吸慢病管理研究"创新单元,北京 100730
- 折叠
摘要
针对手工睡眠分期过程繁琐,自动睡眠分期模型存在精度不足或难以解释分类结果的问题,本研究提出了一种基于混合智能的自动睡眠分期模型,结合数据智能和知识智能以实现睡眠分期精度、可解释性和泛化性的平衡.首先,基于典型脑电(electroencephalography,EEG)和眼电(electrooculography,EOG)通道的任意组合,建立了基于U-Net架构的时序全卷积网络和多任务特征映射结构;其次,通过组合不同睡眠图校正方法,探究了知识智能对粗睡眠图的不同作用方式.本模型在ISRUC和Sleep-EDFx数据集上的F1指标分别为 0.804、0.780.此外,本研究利用知识智能解决了模型得到的粗睡眠图跳变过多、睡眠阶段转换不合理的问题.结果表明,本研究能够为睡眠医师提供有效的判读辅助,在提高临床睡眠分期效率上具有巨大潜力.
Abstract
In view of the cumbersome manual sleep staging process and the insufficient accuracy or the difficulty in interpreting classification results of automatic sleep staging model,we proposed an automatic sleep staging model based on mixed intelligence,which combined data intelligence and knowledge intelligence to achieve a balance of sleep staging accuracy,interpretability and gener-alization.Firstly,based on any combination of typical electroencephalography(EEG)and electrooculography(EOG)channels,a se-quential full convolutional network and multi-task feature mapping structure of U-Net architecture were constructed.Secondly,by com-bining different sleep map correction methods,the different action ways of knowledge intelligence to rough sleep map was explored.The F1 index of this model on the ISRUC and Sleep EDFx datasets were 0.804 and 0.780,respectively.In addition,the knowledge intelli-gence was used to the excessive jump in the rough sleep map and unreasonable transition of sleep stages.This research can provide an effective interpretive aid for sleep physicians,and has great potential in improving the efficiency of clinical sleep staging.
关键词
睡眠分期/混合智能/多任务学习/特征映射/可解释性/睡眠图校正Key words
Sleep stage classification/Hybrid intelligence/Multitask learning/Feature mapping/Interpretability/Sleep graphs cor-rection引用本文复制引用
基金项目
国家重点研发计划(2020YFC2003703)
国家自然科学基金资助项目(62071451)
中国医学科学院医学与健康科技创新工程项目(2019I2M-5-019)
出版年
2024
国家科技期刊平台
NSTL
万方数据