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基于深度学习的跨对象脑电睡眠分期研究

Deep learning based sleep staging research of cross-subject EEG

作者: 张金辉  汪鹏  李蕾 
单位:解放军总医院服务保障中心装备保障室(北京 100853),<br />北京邮电大学人工智能学院(北京 100876),<br />通信作者:张金辉,E-mail: zhangjinhui@301hospital.com.cn;<br />&nbsp; &nbsp; &nbsp; &nbsp; 汪鹏,E-mail: matrixwp98@gmail.com
关键词: 跨对象睡眠分期;脑电;深度学习;AttnSleep模型;类感知损失函数 
分类号:&nbsp;R318.04
出版年·卷·期(页码):2022·41·4(399-404)
摘要:

目的 跨对象脑电睡眠分期是国际顶级会议NeurIPS 2021最新提出的一项挑战性任务,目的是解决当前脑电睡眠分期中主要存在的目标数据不足问题。本文基于深度学习方法对该任务进行了初步探索,通过对数据集的深入分析,结合深度学习AttnSleep(attention-based deep learning approach for sleep stage classification)模型,设计实现了一种基于类感知损失函数(class-aware loss function)的单通道脑电睡眠分期方法。方法 实验数据来自NeurIPS 2021 BEETL Competition任务一官方所提供的跨对象数据集,首先对脑电数据进行标准化预处理,然后使用本文设计的方法进行睡眠分期,并对其结果进行检验。结果 在数据集提供的2个不同年龄组别中,本文方法分别达到了67.33和66.68的任务指标,同时也验证了类感知损失函数的作用。结论 实验结果表明,使用基于类感知损失函数的单通道AttnSleep模型有助于在目标数据不足的情况下提升跨对象脑电睡眠分期的效果。

Objective Cross-subject EEG sleep staging is a challenging task recently proposed by the international top conference NeurIPS 2021, which aims to solve the main problem of insufficient data of objects in the current EEG sleep staging. In this paper, a preliminary exploration of this task is carried out based on the deep learning method. Through thorough analysis of the data set, we design and implement a single-channel EEG sleep staging method based on the AttnSleep model with class-aware loss function. Methods The cross-subject experimental data comes from the official data set provided by the task one in NeurIPS 2021 BEETL Competition. First, the EEG data is standardized in preprocessing, and then the method designed in this paper is used and tested for sleep staging. Results In the two age groups provided by the data set, the method in this paper reached the task indicators of 67.33 and 66.68, respectively. The effect of class-aware loss function is also verified. Conclusions Experimental results show that the use of the single-channel AttnSleep model with the class-aware loss function can help to improve the effect of cross-subject EEG sleep staging in the case of lacking target data. The code will be available on https://github.com/MatrixWP/EEG-sleep-stage-classification.

参考文献:

[1] Luyster FS, Strollo Jr PJ, Zee PC, et al. Sleep: a health imperative[J]. Sleep, 2012, 35(6): 727-734.
[2] Finan PH, Quartana PJ, Remeniuk B, et al. Partial sleep deprivation attenuates the positive affective system: effects across multiple measurement modalities[J]. Sleep, 2017, 40(1): zsw017.
[3] Memar P, Faradji F. A novel multi-class EEG-based sleep stage classification system[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2018, 26(1): 84-95.
[4] Lin YP, Jung TP. Improving EEG-based emotion classification using conditional transfer learning[J]. Frontiers in Human Neuroscience, 2017,11: 334.
[5] Tsinalis O, Matthews PM, Guo Y, et al. Automatic sleep stage scoring with single-channel EEG using convolutional neural networks[EB/OL]. (2016-10-05)[2022-04-29]. https://arxiv.org/abs/1610.01683
[6] Chambon S, Galtier MN, Arnal PJ, et al. A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2018, 26(4): 758-769.
[7] Supratak A, Dong H, Wu C, et al. DeepSleepNet: a model for automatic sleep stage scoring based on raw single-channel EEG[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2017, 25(11): 1998-2008.
[8] Mousavi S, Afghah F, Acharya UR. SleepEEGNet: automated sleep stage scoring with sequence to sequence deep learning approach[J]. PLoS One, 2019, 14(5): e0216456.
[9] Chawla NV, Bowyer KW, Hall LO, et al. SMOTE: synthetic minority over-sampling technique[J]. Journal of Artificial Intelligence Research, 2002, 16: 321-357.
[10] Lin TY, Goyal P, Girshick R, et al. Focal loss for dense object detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 42(2): 318-327.?
[11] Schirrmeister RT, Springenberg JT, Fiederer LDJ, et al. Deep learning with convolutional neural networks for EEG decoding and visualization[J]. Human Brain Mapping, 2017, 38: 5391-5420.
[12] Eldele E, Chen Z, Liu C, et al. An attention-based deep learning approach for sleep stage classification with single-channel EEG[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2021, 29: 809-818.
[13] Huang W, Cheng J, Yang Y, et al. An improved deep convolutional neural network with multi-scale information for bearing fault diagnosis[J]. Neurocomputing, 2019, 359: 77-92.
[14] Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need[C]// 31st Conference on Neural Information Processing Systems(NIPS 2017). Long Beach, CA, USA: NIPS, 2017: 5998-6008.
[15] Kingma DP, Ba J. Adam: a method for stochastic optimization[C]//3rd International Conference on Learning Representations. San Diego, USA: ICLR, 2015: 1?15.
[16] Lawhern VJ, Solon AJ, Waytowich NR, et al. EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces[J]. Journal of Neural Engineering, 2018, 15(5): 056013.
[17] Goldberger AL, Amaral LA, Glass L, et al. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals[J]. Circulation, 2000, 101(23): E215-E220.

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