1. Brankack, J., Kukushka, V. I., Vyssotski, A. L. & Draguhn, A. EEG gamma frequency and sleep-wake scoring in mice: Comparing
two types of supervised classifiers. BRAIN RESEARCH. 1322, 59–71, https://doi.org/10.1016/j.brainres.2010.01.069 (2010).
2. Sunagawa, G. A., Séi, H., Shimba, S., Urade, Y. & Ueda, H. R. FASTER: an unsupervised fully automated sleep staging method for
mice. Genes to Cells. 18, 502–518, https://doi.org/10.1111/gtc.12053 (2013).
3. Rempe, M. J., Clegern, W. C. & Wisor, J. P. An automated sleep-state classification algorithm for quantifying sleep timing and sleepdependent dynamics of electroencephalographic and cerebral metabolic parameters. Nature and Science of Sleep. 7, 85–99, https://
doi.org/10.2147/NSS.S84548 (2015).
4. Suzuki, Y., Sato, M., Shiokawa, H., Yanagisawa, M. & Kitagawa, H. MASC: Automatic sleep stage scoring based on brain and
myoelectric signals. Proceeding of 33rd IEEE International Conference on Data Engineering Workshops (ICDE Workshops 2017).
1489–1496, https://doi.org/10.1109/ICDE.2017.218 (2017).
5. Yamabe, M., Kitagawa, H., Shiokawa, H., Yanagisaawa, M. & Sato, M. Sleep stage analysis of mice using deep learning. Proceeding of
the 79th National Convention of Information Processing Society of Japan (In Japanese). 497–498 (2017).
6. Gao, V., Turek, F. & Vitaterna, M. Multiple classifier systems for automatic sleep scoring in mice. Journal of Neuroscience Methods
264, 33–39, https://doi.org/10.1016/j.jneumeth.2016.02.016 (2016).
7. Yaghouby, F., O’Hara, B. F. & Sunderam, S. Unsupervised Estimation of Mouse Sleep Scores and Dynamics Using a Graphical Model
of Electrophysiological Measurements. International Journal of Neural Systems, 26(4), 1650017, 15 pages, https://doi.org/10.1142/
S0129065716500179 (2016).
8. Miladinović, D. et al. SPINDLE: End-to-end learning from EEG/EMG to extrapolate animal sleep scoring across experimental
settings, labs and species. PLOS Computational Biology 15(4), e1006968, 30 pages, https://doi.org/10.1371/journal.pcbi.1006968
(2018).
9. Rytkönen, K. M., Zitting, J. & Porkka, H. T. Automated sleep scoring in rats and mice using the naive bayes classifier. Journal of
Neuroscience Methods 202(1), 60–64, https://doi.org/10.1016/j.jneumeth.2011.08.023 (2011).
10. Hochreiter, S. & Schmidhuber, J. Long short-term memory. Journal of Neural Computation 9(8), 1735–1780, https://doi.org/10.1162/
neco.1997.9.8.1735 (1997).
11. Gers, F. A., Schmidhuber, J. & Cummins, F. A. Learning to forget: Continual prediction with lstm. Neural Computation 12,
2451–2471, https://doi.org/10.1162/089976600300015015 (2000).
12. Kiranyaz, S., Ince, T. & Gabbouj, M. Real-time patient-specific ECG classification by 1-D convolutional neural networks. IEEE
Transaction on Biomedical Engineering. 63(3), 664–675, https://doi.org/10.1109/TBME.2015.2468589 (2016).
13. Fan, X. et al. Multiscaled fusion of deep convolutional neural networks for screening atrial fibrillation from single lead short ECG
recordings. IEEE Journal of Biomedical and Health Informatics. 22(6), 1744–1753, https://doi.org/10.1109/JBHI.2018.2858789
(2018).
14. Wang, F. et al. Analysis for early seizure detection system based on deep learning algorithm. Proceeding of 2018 IEEE International
Conference on Bioinformatics and Biomedicine (BIBM). 2382–2389, https://doi.org/10.1109/BIBM.2018.8621089 (2018).
15. Cecotti, H. & Graser, A. Convolutional neural networks for P300 detection with application to brain-Computer interfaces. IEEE
Transactions on Pattern Analysis and Machine Intelligence. 33(3), 433–445, https://doi.org/10.1109/TPAMI.2010.125 (2011).
16. Graves, A. & Schmidhuber, J. Framewise phoneme classification with bidirectional lstm and other neural network architectures.
Neural Networks. 18(5), 602–610, https://doi.org/10.1016/j.neunet.2005.06.042 (2005).
17. Funato, H. et al. Forward-genetics analysis of sleep in randomly mutagenized mice. Nature. 539, 378–383, https://doi.org/10.1038/
nature20142 (2016).
18. Supratak, A., Dong, H., Wu, C. & Guo, Y. Deepsleepnet: A model for automatic sleep stage scoring based on raw single-channel eeg.
IEEE transactions on Neural System and Rehabilitation Engineering. 25(11), 1998–2008, https://doi.org/10.1109/
TNSRE.2017.2721116 (2017).
19. Kingma, D. P. & Ba, J. Adam: A method for stochastic optimization. Published as a conference paper at the 3rd International
Conference for Learning Representations, 15 (2015).
20. Landis, J. R. & Koch, G. G. The measurement of observer agreement for categorical data. Biometrics. 33(1), 159–174 (1977).
21. Honda, T. et al. A single phosphorylation site of SIK3 regulates daily sleep amounts and sleep need in mice. Proceedings of the National
Academy of Sciences of the United States of America. 115(41), 10458–10463, https://doi.org/10.1073/pnas.1810823115 (2018).
22. Chemelli, R. M. et al. Narcolepsy in orexin knockout mice. Cell. 98(4), 437–451, https://doi.org/10.1016/S0092-8674(00)81973-X
(1999).
23. Pedregosa, F. et al. Scikit-learn: Machine learning in python. Journal of Machine Learning Research 12, 2825–2830 (2011).
24. Abadi, M. et al. TensorFlow:Large-scale machine learning on heterogeneous systems. https://static.googleusercontent.com/media/
research.google.com/en//pubs/archive/45166.pdf Software available from tensorflow.org (2015).
25. Tsinalis, O., Matthews, P. M., Guo, Y. & Zafeiriou, S. Automatic sleep stage scoring with single-channel EEG using convolutional neural
networks. Preprint at https://arxiv.org/abs/1610.01683 (2016).
26. Biswal, S. et al. Expert-level sleep scoring with deep neural networks. Journal of the American Medical Informatics Association,
25(12), 1643–1650, https://doi.org/10.1093/jamia/ocy131 (2018).
27. Aggarwal, K., Khadanga, S., Joty, S. R., Kazaglis, L. & Srivastava, J. A structured learning approach with neural conditional random
fields for sleep staging. Proceeding of 2018 IEEE International Conference on Big Data (Big Data). 1318–1327, https://doi.org/10.1109/
BigData.2018.8622286 (2018).
Acknowledgements
This work was partly supported by the MEXT Program for Building Regional Innovation Ecosystems, MEXT Grantin-Aid for Scientific Research on Innovative Areas, Grant Number 15H05942Y “Living in Space”, the WPI program
from Japan’s MEXT, JSPS KAKENHI Grant Number 17H06095, and FIRST program from JSPS. The authors would
like to thank C. Miyoshi, N. Hotta-Hirashima, A. Ikkyu, and S. Kanno for assisting in the measurement of EEG and
EMG signals of mice. In addition, we are grateful to L. Ota for reproducing the experiments.
Author contributions
K. Horie and H. Kitagawa provided ideas for developing MC-SleepNet; M. Yamabe implemented MC-SleepNet;
M. Yamabe, K. Horie, H. Shiokawa, and H. Kitagawa designed evaluation experiments; M. Yamabe performed the
experiments; M. Yamabe, K. Horie, H. Shiokawa, and H. Kitagawa analyzed the experimental results; H. Funato
and M. Yanagisawa provided the dataset and background knowledge about mice sleep; and M. Yamabe, K. Horie,
H. Kitagawa wrote the paper.
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Competing interests
The authors declare no competing interests.
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