[1]李倩玉,王蓓,金晶,等.基于双向LSTM卷积网络与注意力机制的自动睡眠分期模型[J].智能系统学报,2022,17(3):523-530.[doi:10.11992/tis.202103013]
LI Qianyu,WANG Bei,JIN Jing,et al.Automatic sleep staging model based on the bi-directional LSTM convolutional network and attention mechanism[J].CAAI Transactions on Intelligent Systems,2022,17(3):523-530.[doi:10.11992/tis.202103013]
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LI Qianyu,WANG Bei,JIN Jing,et al.Automatic sleep staging model based on the bi-directional LSTM convolutional network and attention mechanism[J].CAAI Transactions on Intelligent Systems,2022,17(3):523-530.[doi:10.11992/tis.202103013]
基于双向LSTM卷积网络与注意力机制的自动睡眠分期模型
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
17
期数:
2022年第3期
页码:
523-530
栏目:
学术论文—机器感知与模式识别
出版日期:
2022-05-05
- Title:
- Automatic sleep staging model based on the bi-directional LSTM convolutional network and attention mechanism
- Keywords:
- sleep staging; electroencephalogram; convolutional neural network; residual network; Bi-directional long short-term memory network; attention mechanism; class imbalance; oversampling
- 分类号:
- TP391.4
- 摘要:
- 针对现阶段深度睡眠分期模型存在的梯度消失、对时序信息学习能力较弱等问题，提出一种基于双向长短时记忆卷积网络与注意力机制的自动睡眠分期模型。将少样本类别的睡眠脑电数据通过过采样方式进行数据增强后，利用带残差块的卷积神经网络学习数据特征表示，再通过带注意力层的双向长短时记忆网络挖掘深层时序信息，使用Softmax层实现睡眠分期的自动判别。实验使用Sleep-EDF数据集中19晚单通道脑电信号对模型进行交叉验证，取得了较高的分类准确率和宏平均F1值，优于对比方法。该方法能够有效缓解睡眠分期判别中少数类分类性能较低的问题，并提高了深度睡眠分期模型的整体分类性能。
- Abstract:
- Owing to the problems of gradient disappearance and the weak learning ability of time series information in the current deep sleep staging model, we propose an automatic sleep staging model based on the bi-directional long short-tern memory convolutional network and attention mechanism. After the sleep EEG data of minority classes are enhanced by oversampling, a convolutional neural network with a residual block is designed to learn the data feature representation, and then, an attention layer is combined with the BiLSTM network to extract the deep time sequence information, a softmax layer is adopted to realize the automatic discrimination of sleep stages. A total of 19-night single-channel EEG signals from the Sleep-EDF dataset are analyzed to cross-verify the proposed model. The obtained classification accuracy and macro-F1-score (MF1) are more satisfied than the comparison methods. The effect of low classification performance of minority classes in sleep staging is reduced effectively. The overall classification performance by the proposed deep sleep staging model is sufficiently improved.
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- 相似文献/References:
-
[1]陈玲玲,毕晓君.多模态融合网络的睡眠分期研究[J].智能系统学报,2022,17(6):1194.[doi:10.11992/tis.202202018]
CHEN Lingling,BI Xiaojun.Sleep staging model based on multimodal fusion[J].CAAI Transactions on Intelligent Systems,2022,17():1194.[doi:10.11992/tis.202202018]
备注/Memo
收稿日期:2021-03-08。
基金项目:国家自然科学基金项目（61773164）；上海市自然科学基金项目（16ZR1407500）.
作者简介:李倩玉，硕士研究生，主要研究方向为深度学习、生物电信号;王蓓，副研究员，主要研究方向为智能信息处理和模式分类、复杂系统及其在人工生命科学中的应用。主持国家自然科学基金项目、上海市科委科技创新行动计划等5项。发表学术论文50余篇;金晶，教授，博士生导师，主要研究方向为脑–机接口、信号处理和模式识别。主持国家级和省部级科研项目20余项。发表学术论文130余篇
通讯作者:王蓓.E-mail：beiwang@ecust.edu.cn
更新日期/Last Update:
1900-01-01