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Sleep staging from single-channel EEG with multi-scale feature and contextual information

  • Kun Chen
  • Cheng Zhang
  • Jing Ma
  • Guangfa Wang
  • Jue ZhangEmail author
Sleep Breathing Physiology and Disorders • Original Article
  • 115 Downloads

Abstract

Purpose

Portable sleep monitoring devices with less-attached sensors and high-accuracy sleep staging methods can expedite sleep disorder diagnosis. The aim of this study was to propose a single-channel EEG sleep staging model, SleepStageNet, which extracts sleep EEG features by multi-scale convolutional neural networks (CNN) and then infers the type of sleep stages by capturing the contextual information between adjacent epochs using recurrent neural networks (RNN) and conditional random field (CRF).

Methods

To verify the feasibility of our model, two datasets, one composed by two different single-channel EEGs (Fpz-Cz and Pz-Oz) on 20 healthy people and one composed by a single-channel EEG (F4-M1) on 104 obstructive sleep apnea (OSA) patients with different severities, were examined. The corresponding sleep stages were scored as four states (wake, REM, light sleep, and deep sleep). The accuracy measures were obtained from epoch-by-epoch comparison between the model and PSG scorer, and the agreement between them was quantified with Cohen’s kappa (ҡ).

Results

Our model achieved superior performance with average accuracy (Fpz-Cz, 0.88; Pz-Oz, 0.85) and ҡ (Fpz-Cz, 0.82; Pz-Oz, 0.77) on the healthy people. Furthermore, we validated this model on the OSA patients with average accuracy (F4-M1, 0.80) and ҡ (F4-M1, 0.67). Our model significantly improved the accuracy and ҡ compared to previous methods.

Conclusions

The proposed SleepStageNet has proved feasible for assessment of sleep architecture among OSA patients using single-channel EEG. We suggest that this technological advancement could augment the current use of home sleep apnea testing.

Keywords

Sleep staging Single-channel EEG Multi-scale feature Recurrent neural network Conditional random field 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no competing of interests.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. For this type of study, formal consent is not required.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Academy for Advanced Interdisciplinary StudiesPeking UniversityBeijingChina
  2. 2.Department of Respiratory and Critical Care MedicinePeking University First HospitalBeijingChina
  3. 3.College of EngineeringPeking UniversityBeijingChina

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