Abstract
Almost all of the studies in the literature of sleep stage classification are based on traditional statistical learning techniques from a set of extracted features, which need a relative amount of time and effort. Deep learning offers approaches able to automatically extract patterns and abstractions from different types of data (images, sound, biomedical signals, etc.) to perform classification. However, the application of these techniques in the automatic sleep stage scoring field is less widespread to date. This paper proposes a new approach based on a multi-state deep learning neural network architecture, which we named Asymmetrical Multi-State Neural Network. This new network is able to merge two different neural networks, based on two different architectures receiving different input data: single-channel EEG raw signal in time and the respective spectrum. The proposed Asymmetrical Multi-State Neural Network shows to enhance the separated networks’ performance for the given problem on a complete well-known sleep database.
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References
Rechtschaffen, A., Kales, A.: A manual of standardized terminology, techniques and scoring system for sleep stages of human subjects. Public Health Service, U.S. Government Printing Office, Washington, D.C. (1968)
American Academy of Sleep Medicine. The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specification (2007)
Norman, R., Pal, I., Stewart, C., Walsleben, J., Rappaport, D.: Interobserver agreement among sleep scorers from different centers in a large dataset. Sleep 23, 901–908 (2000)
Kerkeni, N., Alexandre, F., Bedoui, M.H., Bougrain, L., Dogui, M.: Automatic classification of sleep stages on a EEG signal by artificial neural networks. In: 5th WSEAS International Conference on SIGNAL, SPEECH and IMAGE, Wisconsin, USA, pp. 1–13 (2005)
Herrera, L.J., Fernandes, C.M., Mora, A., Migotina, D., Largo, R., Guillén, A., Rosa, A.: Combination of heterogeneous EEG feature extraction methods and stacked sequential learning for sleep stage classification. Int. J. Neural Syst. 23(3), 1350012 (2013)
Längkvist, M., Karlsson, L., Loutfi, A.: Sleep stage classification using unsupervised feature learning. Adv. Artif. Neu. Sys. 5, 5 (2012)
Manzano, M., Guillén, A., Rojas, I., Herrera, L.J.: Deep learning using EEG data in time and frequency domains for sleep stage classification. In: Rojas, I., Joya, G., Catala, A. (eds.) IWANN 2017. LNCS, vol. 10305, pp. 132–141. Springer, Cham (2017). doi:10.1007/978-3-319-59153-7_12
Goldberger, A.L., Amaral, L.A.N., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.-K., Stanley, H.E.: PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101(23), e215–e220, 13 June 2000. doi:10.1161/01.CIR.101.23.e215. Circulation Electronic Pages: http://circ.ahajournals.org/content/101/23/e215.full PMID:1085218
Tsinalis, O., Matthews, P.M., Guo, Y., Zafeiriou, S.: Automatic sleep stage scoring with single-channel EEG using convolutional neural networks. Biomed. Eng./Biomed. Tech. arXiv:1610.01683 [stat.ML] (2016)
Zhang, J., Wu, Y., Bai, J., Chen, F.: Automatic sleep stage classification based on sparse deep belief net and combination of multiple classifiers. Trans. Inst. Measur. Control 38(4), 435–451 (2015)
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press (2016). http://www.deeplearningbook.org
Abadi, M., et al.: TensorFlow: Large-scale machine learning on heterogeneous systems (2015). Software available from tensorflow.org
Reddy, A.G., Narava, S.: Artifact removal from eeg signals. Int. J. Comput. Appl. 77(13), 17–19 (2013)
Palaz, D., Magimai-Doss, M., Collobert, R.: Analysis of cnn-based speech recognition system using raw speech as input. In: Proceedings of Interspeech, number Idiap-RR-23-2015, pp. 11–15 (2015)
Yang, J.B., Nguyen, M.N., San, P.P., Li, X.L., Krishnaswamy, S.: Deep convolutional neural networks on multichannel time series for human activity recognition. In: Proceedings of the 24th International Conference on Artificial Intelligence, IJCAI 2015, pp. 3995–4001, AAAI Press (2015)
Abdel-Hamid, O., Mohamed, A. R., Jiang, H., Penn, G.: Applying convolutional neural networks concepts to hybrid nn-hmm model for speech recognition. In: 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4277–4280 (2012)
Tóth, L.: Combining time and frequency-domain convolution in convolutional neural network-based phone recognition. In: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 190–194 (2014)
Abdel-Hamid, D.Y.O., Deng, L.: Exploring convolutional neural network structures and optimization techniques for speech recognition. In: Interspeech (2013)
Shufni, S. A., Mashor, M.Y.: Ecg signals classification based on discrete wavelet transform, time domain and frequency domain features. In: 2nd International Conference on Biomedical Engineering (ICoBE), pp. 1–6 (2015)
Cecotti, H.: A time–frequency convolutional neural network for the offline classification of steady-state visual evoked potential responses. Pattern Recogn. Lett. 32(8), 1145–1153 (2011)
Tuncer, E., Bolat E.D.: Eeg signal based sleep stage classification using discrete wavelet transform. In: International Conference on Chemistry, Biomedical and Environment Engineering (2014)
Sermanet, P., LeCun, Y.: Traffic sign recognition with multi-scale convolutional networks. In: The 2011 International Joint Conference on Neural Networks, pp. 2809–2813 (2011)
Bertasius, G., Shi, J., Torresani, L.: Deepedge: a multi-scale bifurcated deep network for top-down contour detection. CoRR, abs/1412.1123 (2014)
Zheng, Y., Liu, Q., Chen, E., Ge, Y., Zhao, J.L.: Time series classification using multi-channels deep convolutional neural networks. In: Li, F., Li, Gu., Hwang, S., Yao, B, Zhang, Z. (eds.) WAIM 2014. LNCS, vol. 8485, pp. 298–310. Springer, Cham (2014). doi:10.1007/978-3-319-08010-9_33
Song, Y., McLoughlin, I.V., Dai, L.: Deep bottleneck feature for image classification. In: Proceedings of the 5th ACM on International Conference on Multimedia Retrieval, NY, USA, pp. 491–494 (2015)
Bresler, M., Sheffy, K., Pillar, G., Preiszler, M., Herscovici, S.: Differentiating between light and deep sleep stages using an ambulatory device based on peripheral arterial tonometry. Physiol. Meas. 29(5), 571 (2008)
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)
The sleep-edf database [expanded]. https://www.physionet.org/physiobank/database/sleep-edfx/
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Manzano, M., Guillén, A., Rojas, I., Herrera, L.J. (2017). Combination of EEG Data Time and Frequency Representations in Deep Networks for Sleep Stage Classification. In: Huang, DS., Jo, KH., Figueroa-García, J. (eds) Intelligent Computing Theories and Application. ICIC 2017. Lecture Notes in Computer Science(), vol 10362. Springer, Cham. https://doi.org/10.1007/978-3-319-63312-1_20
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