Abstract
A normal person spends about third of his life in sleep. Healthy sleep is vital to people’s normal lives. Sleep analysis can be used to diagnose certain physiological and neurological diseases such as insomnia and narcolepsy. This paper will introduce the sleep stage and the corresponding electroencephalogram (EEG) characteristics at each stage. We used the deep convolutional neural network (CNN) to classify original EEG data with narcolepsy. We use perturbations based on frequency to generate adversarial examples to analyze the characteristics of narcolepsy in different sleep stages. We find that perturbations at specific frequencies affect the classification results of deep learning.
Supported by the NSFC (No. 61332013 and No. 61672161).
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Acknowledgment
This paper is supported by the National Science Foundation of China (No. 61332013 and No. 61672161).
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Wang, J., Zhang, Y., Ma, Q. (2018). Analysis of Narcolepsy Based on Single-Channel EEG Signals. In: Mondal, A., Gupta, H., Srivastava, J., Reddy, P., Somayajulu, D. (eds) Big Data Analytics. BDA 2018. Lecture Notes in Computer Science(), vol 11297. Springer, Cham. https://doi.org/10.1007/978-3-030-04780-1_20
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DOI: https://doi.org/10.1007/978-3-030-04780-1_20
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