[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]
点击复制

基于双向LSTM卷积网络与注意力机制的自动睡眠分期模型

参考文献/References:
[1] LUYSTER F S, STROLLO P J, ZEE P C, et al. Sleep: a health imperative[J]. Sleep, 2012, 35(6): 727–734.
[2] IBER C, ANCOLI-ISRAEL S, CHESSON A L, et al. The AASM manual for the scoring of sleep and associated events: rules, terminology and technical specifications[M]. Westchester, IL: American Academy of Sleep Medicine, 2007.
[3] RECHTSCHAFFEN A, KALES A. A manual of standardized terminology: techniques and scoring system of sleep stages of human subjects[M]. Los Angeles: UCLA Brain Information Service/Brain Research Institute, 1968.
[4] BERRY R B, BUDHIRAJA R, GOTTLIEB D J, et al. Rules for scoring respiratory events in sleep: update of the 2007 AASM manual for the scoring of sleep and associated events[J]. Journal of clinical sleep medicine, 2012, 8(5): 597–619.
[5] KARSON C N. Brainstem control of wakefulness and sleep[J]. American journal of psychiatry, 1991, 148(7): 944.
[6] ROEBUCK A, MONASTERIO V, GEDERI E, et al. A review of signals used in sleep analysis[J]. Physiological measurement, 2014, 35(1): R1–R57.
[7] FAUST O, RAZAGHI H, BARIKA R, et al. A review of automated sleep stage scoring based on physiological signals for the new millennia[J]. Computer methods and programs in biomedicine, 2019, 176: 81–91.
[8] DIYKH M, LI Yan, WEN Peng. EEG sleep stages classification based on time domain features and structural graph similarity[J]. IEEE transactions on neural systems & rehabilitation engineering, 2016, 24(11): 1159–1168.
[9] EBRAHIMI F, SETAREHDAN S K, NAZERAN H. Automatic sleep staging by simultaneous analysis of ECG and respiratory signals in long epochs[J]. Biomedical signal processing and control, 2015, 18(4): 69–79.
[10] DIMITRIADIS S I, SALIS C, LINDEN D. A novel, fast and efficient single-sensor automatic sleep-stage classification based on complementary cross-frequency coupling estimates[J]. Clinical neurophysiology, 2018, 129(4): 815–828.
[11] DA SILVEIRA T L T, KOZAKEVICIUS A J, RODRIGUES C R. Single-channel EEG sleep stage classification based on a streamlined set of statistical features in wavelet domain[J]. Medical and biological engineering and computing, 2017, 55(2): 343–352.
[12] LAJNEF T, CHAIBI S, RUBY P, et al. Learning machines and sleeping brains: Automatic sleep stage classification using decision-tree multi-class support vector machines[J]. Journal of neuroscience methods, 2015, 250: 94–105.
[13] LIU Junbiao, WU Duanpo, WANG Zimeng, et al. Automatic sleep staging algorithm based on random forest and hidden markov model[J]. CMES-computer modeling in engineering and sciences, 2020, 123(1): 401–426.
[14] ALICKOVIC E, SUBASI A. Ensemble SVM method for automatic sleep stage classification[J]. IEEE transactions on instrumentation and measurement, 2018, 67(6): 1258–1265.
[15] BASHA A J, BALAJI B S, POORNIMA S, et al. Support vector machine and simple recurrent network based automatic sleep stage classification of fuzzy kernel[J]. Journal of ambient intelligence and humanized computing, 2021, 12(6): 6189–6197.
[16] TSINALIS O, MATTHEWS P M, GUO Yike, et al. Automatic sleep stage scoring with single-channel EEG using convolutional neural networks[EB/OL]. (2016–10–05)[2021–03–08] https://arxiv.org/abs/1610.01683.
[17] ZHAO Ranqi, XIA Yi, WANG Qiuyang. Dual-modal and multi-scale deep neural networks for sleep staging using EEG and ECG signals[J]. Biomedical signal processing and control, 2021, 66: 102455.
[18] SORS A, BONNET S, MIREK S, et al. A convolutional neural network for sleep stage scoring from raw single-channel EEG[J]. Biomedical signal processing and control, 2018, 42: 107–114.
[19] HSU Y L, YANG Yating, WANG J S, et al. Automatic sleep stage recurrent neural classifier using energy features of EEG signals[J]. Neurocomputing, 2013, 104: 105–114.
[20] 杨鑫, 吴之南, 钱松荣. 基于双向递归神经网络的单通道脑电图睡眠分期研究[J]. 微型电脑应用, 2017, 33(1): 42–45
YANG Xin, WU Zhinan, QIAN Songrong. Study on sleep staging based on bidirectional recurrent neural network in single channel EEG[J]. Microcomputer applications, 2017, 33(1): 42–45
[21] KUO C E, CHEN Guanting. Automatic sleep staging based on a hybrid stacked LSTM neural network: verification using large-scale dataset[J]. IEEE access, 2020, 8: 111837–111849.
[22] CASCIOLA A A, CARLUCCI S K, KENT B A, et al. A deep learning strategy for automatic sleep staging based on two-channel EEG headband data[J]. Sensors, 2021, 21(10): 3316.
[23] DONG Hao, SUPRATAK A, PAN Wei, et al. Mixed neural network approach for temporal sleep stage classification[J]. IEEE transactions on neural systems and rehabilitation engineering, 2018, 26(2): 324–333.
[24] 罗森林, 郝靖伟, 潘丽敏. 基于CNN-BiLSTM的自动睡眠分期方法[J]. 北京理工大学学报, 2020, 40(7): 746–752
LUO Senlin, HAO Jingwei, PAN Limin. An automatic sleep staging method based on CNN-BiLSTM[J]. Transactions of Beijing Institute of Technology, 2020, 40(7): 746–752
[25] CHEN Xueyan, HE Jie, WU Xiaoqiang, et al. Sleep staging by bidirectional long short-term memory convolution neural network[J]. Future generation computer systems, 2020, 109: 188–196.
[26] HAN Han, WANG Wenyuan, MAO Binghuan. Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning[C]//International Conference on Intelligent Computing. Fuzhou: Springer, 2015: 878–887.
[27] GOLDBERGER A L, AMARAL L A, 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.
[28] SUPRATAK A, DONG Hao, WU Chao, 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.
[29] MOUSAVI S, AFGHAH F, ACHARYA U R. SleepEEGNet: automated sleep stage scoring with sequence to sequence deep learning approach[J]. PLoS one, 2019, 14(5): e0216456.
[30] 马家睿, 王蓓, 金晶, 等. 结合状态转移规则的深度睡眠分期模型[J]. 计算机工程与设计, 2020, 41(10): 2878–2883
MA Jiarui, WANG Bei, JIN Jing, et al. Deep automatic sleep staging model integrated with state transition rules[J]. Computer engineering and design, 2020, 41(10): 2878–2883
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
Copyright © 《 智能系统学报》 编辑部
地址:(150001)黑龙江省哈尔滨市南岗区南通大街145-1号楼 电话:0451- 82534001、82518134 邮箱:tis@vip.sina.com