research-article

A Study on Automatic Sleep Stage Classification Based on CNN-LSTM

Authors:

Xiangwei Zheng, and

Feng YuanAuthors Info & Claims

ICCSE'18: Proceedings of the 3rd International Conference on Crowd Science and Engineering

July 2018

Article No.: 4, Pages 1 - 5

https://doi.org/10.1145/3265689.3265693

Published: 28 July 2018 Publication History

Abstract

Automatic Sleep Stage Classification (ASSC) plays an important role in the diagnosis of sleep related diseases. However, due to the complexity of mathematical modelling, ASSC has many difficulties. At the same time, the rapid fluctuations between the adjacent sleep stages make it difficult to extract features, resulting in an inaccurate classification of a period of electroencephalogram (EEG) sleep. In order to solve the above problems, this paper proposes a sleep stage classification method based on convolutional neural network and long-term short-term memory network (CNN-LSTM). The method applies CNN to extract spatial features from the original data and LSTM to extract temporal features and adopt softmax to classify these features. To verify the proposed method, we tested it on a public data set called ISRUC-Sleep and compared it with several state-of-the-art methods. The experimental results show that the proposed method significantly improves the accuracy of sleep staging and achieves better results.

References

[1]

Fava C, Montagnana M, Favaloro EJ, Guidi GC, Lippi G. Obstructive sleep apnea syndrome and cardiovascular diseases. Semin Thromb Hemost 2011; 37: 280--297.

[2]

Chambon S, Galtier M N, Arnal P J, et al. A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series{J}. IEEE Transactions on Neural Systems & Rehabilitation Engineering, 2018, PP (99): 1--1.

[3]

Ronzhina M, Janoušek O, Kolářová J, et al. Sleep scoring using artificial neural networks {J}. Sleep medicine reviews, 2012, 16(3): 251--263.

[4]

Roberts S, Tarassenko L. New method of automated sleep quantification. {J}. Medical & Biological Engineering & Computing, 1992, 30(5): 509--517.

[5]

Lajnef T, Chaibi S, and 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.

[6]

Penzel T, Kantelhardt J W, Becker H F, et al. Detrended fluctuation analysis and spectral analysis of heart rate variability for sleep stage and sleep apnea identification{C}//Computers in Cardiology, 2003. IEEE, 2003: 307--310.

[7]

Weiss B, Clemens Z, Bódizs R, et al. Comparison of fractal and power spectral EEG features: Effects of topography and sleep stages {J}. Brain Research Bulletin, 2011, 84(6): 359--375.

[8]

Tong N, Lu H, Ruan X, et al. Salient object detection via bootstrap learning{C}//Computer Vision and Pattern Recognition (CVPR), 2015 IEEE Conference on. IEEE, 2015: 1884--1892.

[9]

Liping Gao, Bin Shao, Lin Zhu, Tun Lu and Ning Gu. Maintaining Time and Space Consistencies in Hybrid Engineering Environments: Framework and Algorithms. Computers in Industry. Volume. 59 (2008), Issue. 9. pp: 894--904.

Digital Library

[10]

Stober S, Cameron D J, Grahn J A. Using Convolutional Neural Networks to Recognize Rhythm Stimuli from Electroencephalography Recordings{C}//Advances in neural information processing systems. 2014: 1449--1457.

Digital Library

[11]

Cintas C, Quinto-Sánchez M, Acuña V, et al. Automatic ear detection and feature extraction using Geometric Morphometrics and convolutional neural networks {J}. IET Biometrics, 2016, 6(3): 211--223.

[12]

Wulsin D, Blanco J, Mani R, et al. Semi-Supervised Anomaly Detection for EEG Waveforms Using Deep Belief Nets{C}// Ninth International Conference on Machine Learning and Applications. IEEE, 2011: 436--441.

Digital Library

[13]

Schuster.M and Paliwal.K, 0"Bidirectional recurrent neural networks,"IEEE Trans. Signal Process., vol. 45, no. 11, pp. 2673--2681, 1997.

Digital Library

[14]

Pascanu.O, Mikolov.T, and Bengio.Y, "On the difficulty of training Recurrent Neural Networks," arXiv preprint arXiv:1211.5063, 2012.

[15]

Tsinalis O, Matthews P M, Guo Y, et al. Automatic Sleep Stage Scoring with Single-Channel EEG Using Convolutional Neural Networks{J}. 2016.

Cited By

Jayalakshmi PManimegalai PSankar J(2024)An Ample Review of Various Deep Learning Skills for Identifying the Stages of SleepBiomedical Engineering Science and Technology10.1007/978-3-031-54547-4_5(47-65)Online publication date: 15-Mar-2024
https://doi.org/10.1007/978-3-031-54547-4_5
Kaliyapillai SKrishnamurthy SMurugasamy T(2023)An Ensemble of Voting- based Deep Learning Models with Regularization Functions for Sleep Stage ClassificationAdvances in Science, Technology and Engineering Systems Journal10.25046/aj0801108:1(84-94)Online publication date: Jan-2023
https://doi.org/10.25046/aj080110
Liang HLiu YWang HJia ZElkind E(2023)Teacher assistant-based knowledge distillation extracting multi-level features on single channel sleep EEGProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence10.24963/ijcai.2023/439(3948-3956)Online publication date: 19-Aug-2023
https://dl.acm.org/doi/10.24963/ijcai.2023/439
Show More Cited By

Index Terms

A Study on Automatic Sleep Stage Classification Based on CNN-LSTM
1. Human-centered computing
  1. Collaborative and social computing

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cover image ACM Other conferences

ICCSE'18: Proceedings of the 3rd International Conference on Crowd Science and Engineering

July 2018

220 pages

ISBN:9781450365871

DOI:10.1145/3265689

Copyright © 2018 ACM.

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Association for Computing Machinery

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Publication History

Published: 28 July 2018

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ICCSE'18

ICCSE'18: The 3rd International Conference on Crowd Science and Engineering

July 28 - 31, 2018

Singapore, Singapore

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ICCSE'18 Paper Acceptance Rate 33 of 89 submissions, 37%;

Overall Acceptance Rate 92 of 247 submissions, 37%

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Cited By

Jayalakshmi PManimegalai PSankar J(2024)An Ample Review of Various Deep Learning Skills for Identifying the Stages of SleepBiomedical Engineering Science and Technology10.1007/978-3-031-54547-4_5(47-65)Online publication date: 15-Mar-2024
https://doi.org/10.1007/978-3-031-54547-4_5
Kaliyapillai SKrishnamurthy SMurugasamy T(2023)An Ensemble of Voting- based Deep Learning Models with Regularization Functions for Sleep Stage ClassificationAdvances in Science, Technology and Engineering Systems Journal10.25046/aj0801108:1(84-94)Online publication date: Jan-2023
https://doi.org/10.25046/aj080110
Liang HLiu YWang HJia ZElkind E(2023)Teacher assistant-based knowledge distillation extracting multi-level features on single channel sleep EEGProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence10.24963/ijcai.2023/439(3948-3956)Online publication date: 19-Aug-2023
https://dl.acm.org/doi/10.24963/ijcai.2023/439
Qiao YLuo JCui TLiu HTang HZeng YLiu CLi YJian JWu JTian HYang YRen TZhou J(2023)Soft Electronics for Health Monitoring Assisted by Machine LearningNano-Micro Letters10.1007/s40820-023-01029-115:1Online publication date: 15-Mar-2023
https://doi.org/10.1007/s40820-023-01029-1
Tăuțan ARossi AIonescu B(2022)Automatic sleep scoring with LSTM networks: impact of time granularity and input signalsBiomedical Engineering / Biomedizinische Technik10.1515/bmt-2021-040867:4(267-281)Online publication date: 6-Jun-2022
https://doi.org/10.1515/bmt-2021-0408
Sri TA RMadala JDuddukuru SReddipalli RPolasi P(2022)A Systematic Review on Deep Learning Models for Sleep Stage Classification2022 6th International Conference on Trends in Electronics and Informatics (ICOEI)10.1109/ICOEI53556.2022.9776965(1505-1511)Online publication date: 28-Apr-2022
https://doi.org/10.1109/ICOEI53556.2022.9776965
Loh HOoi CVicnesh JOh SFaust OGertych AAcharya U(2020)Automated Detection of Sleep Stages Using Deep Learning Techniques: A Systematic Review of the Last Decade (2010–2020)Applied Sciences10.3390/app1024896310:24(8963)Online publication date: 15-Dec-2020
https://doi.org/10.3390/app10248963
Tăuţan ARossi Ade Francisco RIonescu B(2020)Dimensionality reduction for EEG-based sleep stage detection: comparison of autoencoders, principal component analysis and factor analysisBiomedical Engineering / Biomedizinische Technik10.1515/bmt-2020-013966:2(125-136)Online publication date: 12-Oct-2020
https://doi.org/10.1515/bmt-2020-0139
Timplalexis CDiamantaras KChouvarda I(2019)Classification of Sleep Stages for Healthy Subjects and Patients with Minor Sleep Disorders2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)10.1109/BIBE.2019.00068(344-351)Online publication date: Oct-2019
https://doi.org/10.1109/BIBE.2019.00068

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