publication . Article . Other literature type . Preprint . 2018

A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time Series

Stanislas Chambon; Mathieu Galtier; Pierrick J. Arnal; Gilles Wainrib; Alexandre Gramfort;
Open Access
  • Published: 07 Mar 2018 Journal: IEEE Transactions on Neural Systems and Rehabilitation Engineering, volume 26, pages 758-769 (issn: 1534-4320, eissn: 1558-0210, Copyright policy)
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • Country: France
Abstract
International audience; Sleep stage classification constitutes an important preliminary exam in the diagnosis of sleep disorders. It is traditionally performed by a sleep expert who assigns to each 30 s of signal a sleep stage, based on the visual inspection of signals such as electroencephalograms (EEG), electrooculograms (EOG), electrocardiograms (ECG) and electromyograms (EMG). We introduce here the first deep learning approach for sleep stage classification that learns end-to-end without computing spectrograms or extracting hand-crafted features, that exploits all multivariate and multimodal Polysomnography (PSG) signals (EEG, EMG and EOG), and that can expl...
Persistent Identifiers
Subjects
ACM Computing Classification System: ComputingMethodologies_PATTERNRECOGNITION
free text keywords: General Neuroscience, Biomedical Engineering, Computer Science Applications, transfer learning, spatio-temporal data, Sleep stage classification, multivariate time se-, EOG, EEG, EMG, ries, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI], [ INFO.INFO-NE ] Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], [ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing, Statistics - Machine Learning, Computer Science - Computer Vision and Pattern Recognition, Quantitative Biology - Neurons and Cognition, Sleep Stages, Spectrogram, Feature extraction, Deep learning, Pattern recognition, Polysomnography, medicine.diagnostic_test, medicine, Classifier (linguistics), Artificial intelligence, business.industry, business, Data segment, Softmax function, Computer science
46 references, page 1 of 4

[1] C. Berthomier, X. Drouot, M. Herman-Sto¨ıca, P. Berthomier, J. Prado, D. Bokar-Thire, O. Benoit, J. Mattout, and M.-P. D'Ortho, “Automatic analysis of single-channel sleep EEG: validation in healthy individuals.,” Sleep, vol. 30, no. 11, pp. 1587-1595, 2007.

[2] C. Iber, S. Ancoli-Israel, A. Chesson, and S. F. Quan, “The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specification,” 2007.

[3] J. Allan Hobson, “A manual of standardized terminology, techniques and scoring system for sleep stages of human subjects,” Electroencephalography and Clinical Neurophysiology, vol. 26, p. 644, June 1969.

[4] O. Tsinalis, P. M. Matthews, and Y. Guo, “Automatic Sleep Stage Scoring Using Time-Frequency Analysis and Stacked Sparse Autoencoders,” Annals of Biomedical Engineering, vol. 44, no. 5, pp. 1587-1597, 2016. [OpenAIRE]

[5] J. B. Stephansen, A. Ambati, E. B. Leary, H. E. M. IV, O. Carrillo, L. Lin, B. Hogl, A. Stefani, S. C. Hong, T. W. Kim, F. Pizza, G. Plazzi, S. Vandi, E. Antelmi, D. Perrin, S. T. Kuna, P. K. Schweitzer, C. Kushida, P. E. Peppard, P. Jennum, H. B. D. Sørensen, and E. Mignot, “The use of neural networks in the analysis of sleep stages and the diagnosis of narcolepsy,” CoRR, vol. abs/1710.02094, 2017.

[6] R. S. Rosenberg and S. Van Hout, “the American Academy of sleep Medicine Inter-scorer Reliability Program: Sleep Stage Scoring,” Journal of Clinical Sleep Medicine, vol. 10, no. 4, pp. 447-454, 2014.

[7] K. Aboalayon, M. Faezipour, W. Almuhammadi, and S. Moslehpour, “Sleep Stage Classification Using EEG Signal Analysis: A Comprehensive Survey and New Investigation,” Entropy, vol. 18, no. 9, p. 272, 2016. [OpenAIRE]

[8] A. Vilamala, K. H. Madsen, and L. K. Hansen, “Deep convolutional neural networks for interpretable analysis of EEG sleep stage scoring,” CoRR, vol. abs/1710.00633, 2017.

[9] S. Biswal, J. Kulas, H. Sun, B. Goparaju, M. B. Westover, M. T. Bianchi, and J. Sun, “SLEEPNET: automated sleep staging system via deep learning,” CoRR, vol. abs/1707.08262, 2017. [OpenAIRE]

[10] H. Dong, A. Supratak, W. Pan, C. Wu, P. M. Matthews, and Y. Guo, “Mixed Neural Network Approach for Temporal Sleep Stage Classification,” arXiv:1610.06421v1, vol. 1, 2016.

[11] O. Tsinalis, P. M. Matthews, Y. Guo, and S. Zafeiriou, “Automatic Sleep Stage Scoring with Single-Channel EEG Using Convolutional Neural Networks,” arXiv:1610.01683, pp. 1-10, 2016.

[12] A. Supratak, H. Dong, C. Wu, and Y. Guo, “Deepsleepnet: a model for automatic sleep stage scoring based on raw single-channel eeg,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2017.

[13] A. Sors, S. Bonnet, S. Mirek, L. Vercueil, and J.-F. Payen, “A convolutional neural network for sleep stage scoring from raw single-channel eeg,” PrePrint, 2017.

[14] M. Zhao, S. Yue, D. Katabi, T. S. Jaakkola, and M. T. Bianchi, “Learning sleep stages from radio signals: A conditional adversarial architecture,” in Proceedings of the 34th International Conference on Machine Learning (D. Precup and Y. W. Teh, eds.), vol. 70 of Proceedings of Machine Learning Research, (International Convention Centre, Sydney, Australia), pp. 4100-4109, PMLR, 06-11 Aug 2017.

[15] H. He and E. A. Garcia, “Learning from imbalanced data,” IEEE Transactions on Knowledge and Data Engineering, vol. 21, pp. 1263- 1284, Sept 2009.

46 references, page 1 of 4
Abstract
International audience; Sleep stage classification constitutes an important preliminary exam in the diagnosis of sleep disorders. It is traditionally performed by a sleep expert who assigns to each 30 s of signal a sleep stage, based on the visual inspection of signals such as electroencephalograms (EEG), electrooculograms (EOG), electrocardiograms (ECG) and electromyograms (EMG). We introduce here the first deep learning approach for sleep stage classification that learns end-to-end without computing spectrograms or extracting hand-crafted features, that exploits all multivariate and multimodal Polysomnography (PSG) signals (EEG, EMG and EOG), and that can expl...
Persistent Identifiers
Subjects
ACM Computing Classification System: ComputingMethodologies_PATTERNRECOGNITION
free text keywords: General Neuroscience, Biomedical Engineering, Computer Science Applications, transfer learning, spatio-temporal data, Sleep stage classification, multivariate time se-, EOG, EEG, EMG, ries, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI], [ INFO.INFO-NE ] Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], [ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing, Statistics - Machine Learning, Computer Science - Computer Vision and Pattern Recognition, Quantitative Biology - Neurons and Cognition, Sleep Stages, Spectrogram, Feature extraction, Deep learning, Pattern recognition, Polysomnography, medicine.diagnostic_test, medicine, Classifier (linguistics), Artificial intelligence, business.industry, business, Data segment, Softmax function, Computer science
46 references, page 1 of 4

[1] C. Berthomier, X. Drouot, M. Herman-Sto¨ıca, P. Berthomier, J. Prado, D. Bokar-Thire, O. Benoit, J. Mattout, and M.-P. D'Ortho, “Automatic analysis of single-channel sleep EEG: validation in healthy individuals.,” Sleep, vol. 30, no. 11, pp. 1587-1595, 2007.

[2] C. Iber, S. Ancoli-Israel, A. Chesson, and S. F. Quan, “The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specification,” 2007.

[3] J. Allan Hobson, “A manual of standardized terminology, techniques and scoring system for sleep stages of human subjects,” Electroencephalography and Clinical Neurophysiology, vol. 26, p. 644, June 1969.

[4] O. Tsinalis, P. M. Matthews, and Y. Guo, “Automatic Sleep Stage Scoring Using Time-Frequency Analysis and Stacked Sparse Autoencoders,” Annals of Biomedical Engineering, vol. 44, no. 5, pp. 1587-1597, 2016. [OpenAIRE]

[5] J. B. Stephansen, A. Ambati, E. B. Leary, H. E. M. IV, O. Carrillo, L. Lin, B. Hogl, A. Stefani, S. C. Hong, T. W. Kim, F. Pizza, G. Plazzi, S. Vandi, E. Antelmi, D. Perrin, S. T. Kuna, P. K. Schweitzer, C. Kushida, P. E. Peppard, P. Jennum, H. B. D. Sørensen, and E. Mignot, “The use of neural networks in the analysis of sleep stages and the diagnosis of narcolepsy,” CoRR, vol. abs/1710.02094, 2017.

[6] R. S. Rosenberg and S. Van Hout, “the American Academy of sleep Medicine Inter-scorer Reliability Program: Sleep Stage Scoring,” Journal of Clinical Sleep Medicine, vol. 10, no. 4, pp. 447-454, 2014.

[7] K. Aboalayon, M. Faezipour, W. Almuhammadi, and S. Moslehpour, “Sleep Stage Classification Using EEG Signal Analysis: A Comprehensive Survey and New Investigation,” Entropy, vol. 18, no. 9, p. 272, 2016. [OpenAIRE]

[8] A. Vilamala, K. H. Madsen, and L. K. Hansen, “Deep convolutional neural networks for interpretable analysis of EEG sleep stage scoring,” CoRR, vol. abs/1710.00633, 2017.

[9] S. Biswal, J. Kulas, H. Sun, B. Goparaju, M. B. Westover, M. T. Bianchi, and J. Sun, “SLEEPNET: automated sleep staging system via deep learning,” CoRR, vol. abs/1707.08262, 2017. [OpenAIRE]

[10] H. Dong, A. Supratak, W. Pan, C. Wu, P. M. Matthews, and Y. Guo, “Mixed Neural Network Approach for Temporal Sleep Stage Classification,” arXiv:1610.06421v1, vol. 1, 2016.

[11] O. Tsinalis, P. M. Matthews, Y. Guo, and S. Zafeiriou, “Automatic Sleep Stage Scoring with Single-Channel EEG Using Convolutional Neural Networks,” arXiv:1610.01683, pp. 1-10, 2016.

[12] A. Supratak, H. Dong, C. Wu, and Y. Guo, “Deepsleepnet: a model for automatic sleep stage scoring based on raw single-channel eeg,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2017.

[13] A. Sors, S. Bonnet, S. Mirek, L. Vercueil, and J.-F. Payen, “A convolutional neural network for sleep stage scoring from raw single-channel eeg,” PrePrint, 2017.

[14] M. Zhao, S. Yue, D. Katabi, T. S. Jaakkola, and M. T. Bianchi, “Learning sleep stages from radio signals: A conditional adversarial architecture,” in Proceedings of the 34th International Conference on Machine Learning (D. Precup and Y. W. Teh, eds.), vol. 70 of Proceedings of Machine Learning Research, (International Convention Centre, Sydney, Australia), pp. 4100-4109, PMLR, 06-11 Aug 2017.

[15] H. He and E. A. Garcia, “Learning from imbalanced data,” IEEE Transactions on Knowledge and Data Engineering, vol. 21, pp. 1263- 1284, Sept 2009.

46 references, page 1 of 4
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