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Deep Learning for Single-Channel EEG Signals Sleep Stage Scoring Based on Frequency Domain Representation

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Health Information Science (HIS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11837))

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Abstract

Sleep is vital to the health of the human being. Accurate sleep stage scoring is an important prerequisite for diagnosing sleep health problems. The sleep electroencephalogram (EEG) waveform shows diverse variations under the physical conditions of subjects. To help neurologists better analyze sleep data in a fairly short time, we decide to develop a novel method to extract features from EEG signals. Traditional sleep stage scoring methods typically extract the one-dimensional (1D) features of single-channel EEG signals. This paper is the very first time to represent the single-channel EEG signals as two-dimensional (2D) frequency domain representation. Comparing with similar currently existing methods, a deep learning model trained by frequency domain representation can extract frequency morphological features over EEG signal patterns. We conduct experiments on the real EEG signals dataset, which is obtained from PhysioBank Community. The experiment results show that our method significantly improved the performance of the classifier.

Supported by the NSFC (No. 61672161), and the DongGuan Innovative Research Team Program No. 2018607201008).

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Notes

  1. 1.

    http://www.physionet.org/physiobank/database/sleep-edfx/.

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Author information

Authors and Affiliations

  1. Shanghai key Laboratory of Data Science, School of Computer Science, Fudan University, Shanghai, China

    Jialin Wang, Huihui Huang & Xiaoyuan Hong

  2. Institute for Sustainable Industries & Liveable Cities, VU Research, Victoria University, Melbourne, Australia

    Yanchun Zhang

  3. Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, China

    Jialin Wang, Yanchun Zhang, Huihui Huang & Xiaoyuan Hong

  4. The First Hospital of Hebei Medical University, Shijiazhuang, China

    Qinying Ma

  5. Institute of Electronics and Information Engineering of UESTC in Guangdong, Dongguan, China

    Yanchun Zhang

Authors
  1. Jialin Wang
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  2. Yanchun Zhang
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  3. Qinying Ma
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  4. Huihui Huang
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  5. Xiaoyuan Hong
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Corresponding author

Correspondence to Yanchun Zhang .

Editor information

Editors and Affiliations

  1. Victoria University, Footscray, VIC, Australia

    Hua Wang

  2. Victoria University, Footscray, VIC, Australia

    Siuly Siuly

  3. Swinburne University of Technology, Hawthorn, VIC, Australia

    Rui Zhou

  4. Instituto de Salud Carlos III, Madrid, Spain

    Fernando Martin-Sanchez

  5. Victoria University, Footscray, VIC, Australia

    Yanchun Zhang

  6. Vrije University, Amsterdam, The Netherlands

    Zhisheng Huang

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Wang, J., Zhang, Y., Ma, Q., Huang, H., Hong, X. (2019). Deep Learning for Single-Channel EEG Signals Sleep Stage Scoring Based on Frequency Domain Representation. In: Wang, H., Siuly, S., Zhou, R., Martin-Sanchez, F., Zhang, Y., Huang, Z. (eds) Health Information Science. HIS 2019. Lecture Notes in Computer Science(), vol 11837. Springer, Cham. https://doi.org/10.1007/978-3-030-32962-4_12

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  • DOI: https://doi.org/10.1007/978-3-030-32962-4_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-32961-7

  • Online ISBN: 978-3-030-32962-4

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