Abstract
Polysomnography analysis for sleeping disorders is a discipline that is showing interest in the development of reliable classifiers to determine the sleep stage. The most common methods shown in the literature bet for classical learning techniques and statistics that are applied to a reduced number of features in order to tackle the computational load. Nowadays, the application of deep learning to the sleep stage classification problem seems very interesting and novel, therefore, this paper presents a first approximation using a single channel and information from the current epoch to perform the classification. The complete Physionet database has been used in the experiments. Deep learning has been applied to the time and frequency domains from the EEG signal obtaining a good performance and promising further work.
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Acknowledgements
This work was supported by Project TIN2015-71873-R (Spanish Ministry of Economy and Competitiveness -MINECO- and the European Regional Development Fund -ERDF).
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Manzano, M., Guillén, A., Rojas, I., Herrera, L.J. (2017). Deep Learning Using EEG Data in Time and Frequency Domains for Sleep Stage Classification. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2017. Lecture Notes in Computer Science(), vol 10305. Springer, Cham. https://doi.org/10.1007/978-3-319-59153-7_12
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DOI: https://doi.org/10.1007/978-3-319-59153-7_12
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