A Lightweight Segmented Attention Network for Sleep Staging by Fusing Local Characteristics and Adjacent Information

计算机科学 残余物 循环神经网络 睡眠(系统调用) 人工智能 深度学习 块(置换群论) 特征提取 编码器 模式识别(心理学) 睡眠阶段 人工神经网络 特征(语言学) 脑电图 多导睡眠图 算法 医学 数学 哲学 精神科 操作系统 语言学 几何学
作者
Wei Zhou,Hangyu Zhu,Ning Shen,Hongyu Chen,Cong Fu,Huan Yu,Feng Shu,Chen Chen,Wei Chen
出处
期刊:IEEE Transactions on Neural Systems and Rehabilitation Engineering [Institute of Electrical and Electronics Engineers]
卷期号:31: 238-247 被引量:20
标识
DOI:10.1109/tnsre.2022.3220372
摘要

Sleep staging is the essential step in sleep quality assessment and sleep disorders diagnosis. However, most current automatic sleep staging approaches use recurrent neural networks (RNN), resulting in a relatively large training burden. Moreover, these methods only extract information of the whole epoch or adjacent epochs, ignoring the local signal variations within epoch. To address these issues, a novel deep learning architecture named segmented attention network (SAN) is proposed in this paper. The architecture can be divided into feature extraction (FE) and time sequence encoder (TSE). The FE module consists of multiple multiscale CNN (MMCNN) and residual squeeze and excitation block (SE block). The former extracts features from multiple equal-length EEG segments and the latter reinforced the features. The TSE module based on a multi-head attention mechanism could capture the temporal information in the features extracted by FE module. Noteworthy, in SAN, we replaced the RNN module with a TSE module for temporal learning and made the network faster. The evaluation of the model was performed on two widely used public datasets, Montreal Archive of Sleep Studies (MASS) and Sleep-EDFX, and one clinical dataset from Huashan Hospital of Fudan University, Shanghai, China (HSFU). The proposed model achieved the accuracy of 85.5%, 86.4%, 82.5% on Sleep-EDFX, MASS and HSFU, respectively. The experimental results exhibited favorable performance and consistent improvements of SAN on different datasets in comparison with the state-of-the-art studies. It also proved the necessity of sleep staging by integrating the local characteristics within epochs and adjacent informative features among epochs.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
小狗完成签到 ,获得积分10
1秒前
2秒前
2秒前
昏睡的实验室小白完成签到,获得积分10
3秒前
3秒前
yar应助大青山采纳,获得10
3秒前
Rondab应助mariawang采纳,获得10
3秒前
yqb发布了新的文献求助10
3秒前
3秒前
一条裸游的鱼完成签到,获得积分10
5秒前
二白关注了科研通微信公众号
5秒前
6秒前
6秒前
超帅听枫发布了新的文献求助10
6秒前
8秒前
8秒前
fish1116发布了新的文献求助100
8秒前
8秒前
bbbbb沫完成签到,获得积分20
8秒前
峥2发布了新的文献求助10
8秒前
wzy完成签到,获得积分10
8秒前
xxxllllll发布了新的文献求助10
9秒前
干净映天完成签到 ,获得积分10
9秒前
LJ_scholar发布了新的文献求助10
10秒前
kuny完成签到,获得积分10
10秒前
勤恳的夏之完成签到,获得积分20
11秒前
wzy发布了新的文献求助10
11秒前
钵钵鸡完成签到 ,获得积分10
11秒前
JING发布了新的文献求助10
11秒前
後zgw完成签到,获得积分10
13秒前
13秒前
14秒前
明朗发布了新的文献求助10
14秒前
meredith0571完成签到,获得积分10
15秒前
15秒前
嗯哼发布了新的文献求助30
15秒前
lucky完成签到 ,获得积分10
15秒前
朴实山彤完成签到,获得积分20
16秒前
16秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3988732
求助须知:如何正确求助?哪些是违规求助? 3531027
关于积分的说明 11252281
捐赠科研通 3269732
什么是DOI,文献DOI怎么找? 1804764
邀请新用户注册赠送积分活动 881869
科研通“疑难数据库(出版商)”最低求助积分说明 809021