基于多头自注意力的自动睡眠分期模型
作者:
作者单位:

作者简介:

通讯作者:

中图分类号:

基金项目:

广东省普通高校特色创新项目(2022KTSCX035); 国家自然科学基金面上项目(62076103)


Automatic Sleep Staging Model Based on Multi-head Self-attention
Author:
Affiliation:

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    睡眠分期在睡眠监测和睡眠质量评估中意义重大, 高精度的睡眠分期能够辅助医师在临床诊断上正确评估睡眠情况. 尽管现有的自动睡眠分期研究已经取得了相对可靠的准确率, 但是仍存在着需要解决的问题: (1)如何更加全面地提取患者的睡眠特征. (2)如何从捕捉到的睡眠特征中获得有效的睡眠状态转换规则. (3)如何有效利用多模态数据提升分类准确率. 为了解决上述问题, 本文提出了基于多头自注意力的自动睡眠分期网络. 为了提取EEG和EOG各自在睡眠阶段中的模态特点, 该网络采用双流并行卷积神经网络结构来分别处理EEG和EOG原数据. 此外, 模型使用由多头自注意力模块和残差网络构成的上下文学习模块来捕捉序列的多方面特征, 学习序列之间的关联性和重要性. 最后模型利用单向LSTM来学习睡眠阶段的过渡规则. 睡眠分期实验结果表明, 本文提出的模型在Sleep-EDF数据集上的总体准确率达到85.7%, MF1分数为80.6%, 且其准确率和鲁棒性优于现有的自动睡眠分期方法, 对自动睡眠分期研究有一定价值.

    Abstract:

    Sleep staging is highly important for sleep monitoring and sleep quality assessment. High-precision sleep staging can assist physicians in correctly evaluating sleep quality during clinical diagnosis. Although existing studies on automatic sleep staging have achieved relatively reliable accuracy, there are still problems that need to be solved: (1) How can sleep features be extracted from patients more comprehensively? (2) How can effective rules for sleep state transition be obtained from the captured sleep features? (3) How can multimodal data be effectively utilized to improve classification accuracy? To solve the above problems, this study proposes an automatic sleep staging network based on multi-head self-attention. To extract the modal characteristics of EEG and EOG in sleep stages separately, this network uses a parallel two-stream convolutional neural network structure to process the original EEG and EOG data separately. In addition, the model uses a contextual learning module, which consists of a multi-head self-attention module and a residual network, to capture the multifaceted features of the sequences and to learn the correlation and significance between the sequences. Finally, the model utilizes unidirectional LSTM to learn the transition rules for sleep stages. The results of the sleep staging experiments show that the model proposed in this study achieves an overall accuracy of 85.7% on the Sleep-EDF dataset, with an MF1 score of 80.6%. Moreover, its accuracy and robustness are better than those of the existing automatic sleep staging methods. This indicates that the proposed model is valuable for automatic sleep staging research.

    参考文献
    相似文献
    引证文献
引用本文

魏婉欣,朱嘉鹏,郑景仁,潘家辉.基于多头自注意力的自动睡眠分期模型.计算机系统应用,2024,33(9):132-139

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2024-03-23
  • 最后修改日期:2024-04-16
  • 录用日期:
  • 在线发布日期: 2024-07-24
  • 出版日期:
文章二维码
您是第位访问者
版权所有:中国科学院软件研究所 京ICP备05046678号-3
地址:北京海淀区中关村南四街4号 中科院软件园区 7号楼305房间,邮政编码:100190
电话:010-62661041 传真: Email:csa (a) iscas.ac.cn
技术支持:北京勤云科技发展有限公司

京公网安备 11040202500063号