Abstract
Sleep stage classification is the categorisation of Electroencephalogram (EEG) epoch into different sleep stages. Various supervised and unsupervised models have been developed for sleep stage classification. Emphasis of those models has been on classifying sleep stages using deep learning models such as the Convolutional Neural Network (CNN), however, very limited work exists on interpreting those CNN filters learned from EEG data in a supervised manner. This paper focuses on investigating and interpreting the output filters of the first CNN layer of the DeepSleepNet model, which is a model developed for automatic sleep stage scoring based on raw Single-Channel EEG. Experiments were carried out using a public benchmark dataset, namely the Sleep EDF Database. Spectral properties of both EEG epoch (input) and the learned filters obtained from the first CNN layer were compared. Results showed similar spectral properties between sleep EEG patterns and the learned filters which were obtained from the first CNN layer, and these findings suggest that ‘sleep stage’-defining EEG patterns are associated with certain learned CNN filters.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Aboalayon, K.A.I., Faezipour, M., Almuhammadi, W.S., Moslehpour, S.: Sleep stage classification using EEG signal analysis: a comprehensive survey and new investigation. Entropy 18(9), 272 (2016)
Biswal, S., Kulas, J., Sun, H., Goparaju, B., Westover, M.B., Bianchi, M.T., Sun, J.: SLEEPNET: automated sleep staging system via deep learning. CoRR abs/1707.08262 (2017)
Howarth, R.J.: Dictionary of Mathematical Geosciences: With Historical Notes, pp. 669–671. Springer, Cham (2017)
Malafeev, A., Laptev, D., Bauer, S., Omlin, X., Wierzbicka, A., Wichniak, A., Jernajczyk, W., Riener, R., Buhmann, J., Achermann, P.: Automatic human sleep stage scoring using deep neural networks. Front. Neurosci. 12, 781 (2018)
Shen, Y., Olbrich, E., Achermann, P., Meier, P.: Dimensional complexity and spectral properties of the human sleep EEG. Clin. Neurophysiol. 114(2), 199–209 (2003)
Sors, A., Bonnet, S., Mirek, S., Vercueil, L., Payen, J.F.: A convolutional neural network for sleep stage scoring from raw single-channel EEG. Biomed. Signal Process. Control 42, 107–114 (2018)
Supratak, A., Dong, H., Wu, C., Guo, Y.: DeepSleepNet: a model for automatic sleep stage scoring based on raw single-channel EEG. IEEE Trans. Neural Syst. Rehabil. Eng. 25(11), 1998–2008 (2017)
Tsinalis, O., Matthews, P.M., Guo, Y., Zafeiriou, S.: Automatic sleep stage scoring with single-channel EEG using convolutional neural networks, October 2016
Tsinalis, O., Matthews, P.M., Guo, Y.: Automatic sleep stage scoring using time-frequency analysis and stacked sparse autoencoders. Ann. Biomed. Eng. 44(5), 1587–1597 (2016)
Vilamala, A., Madsen, K.H., Hansen, L.K.: Deep convolutional neural networks for interpretable analysis of EEG sleep stage scoring. In: 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP), pp. 1–6, September 2017
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Turabee, G., Shen, Y., Cosma, G. (2020). Interpreting the Filters in the First Layer of a Convolutional Neural Network for Sleep Stage Classification. In: Ju, Z., Yang, L., Yang, C., Gegov, A., Zhou, D. (eds) Advances in Computational Intelligence Systems. UKCI 2019. Advances in Intelligent Systems and Computing, vol 1043. Springer, Cham. https://doi.org/10.1007/978-3-030-29933-0_12
Download citation
DOI: https://doi.org/10.1007/978-3-030-29933-0_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-29932-3
Online ISBN: 978-3-030-29933-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)