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

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11837)

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.

Keywords

Sleep stage scoring Data representation Deep learning EEG Fourier transform 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Shanghai key Laboratory of Data Science, School of Computer ScienceFudan UniversityShanghaiChina
  2. 2.Institute for Sustainable Industries & Liveable Cities, VU ResearchVictoria UniversityMelbourneAustralia
  3. 3.Cyberspace Institute of Advanced TechnologyGuangzhou UniversityGuangzhouChina
  4. 4.The First Hospital of Hebei Medical UniversityShijiazhuangChina
  5. 5.Institute of Electronics and Information Engineering of UESTC in GuangdongDongguanChina

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