Sleep stage classification using covariance features of multi-channel physiological signals on Riemannian manifolds.

Journal: Computer methods and programs in biomedicine
Published Date: Sep 1, 2019

Abstract

BACKGROUND AND OBJECTIVE: The recognition of many sleep related pathologies highly relies on an accurate classification of sleep stages. Clinically, sleep stages are usually labelled by sleep experts through visually inspecting the whole-night polysomnography (PSG) recording of patients, wherein electroencephalogram (EEG), electrooculogram (EOG) and electromyogram (EMG) play the dominant role. Developing an automatic sleep staging system based on multi-channel physiological signals could relieve the burden of manual labeling by experts, and obtain reliable and repeatable recognition results as well.

Authors

  • Dihong Jiang
    Department of Electronic Engineering, Fudan University, Shanghai 200433, China. Electronic address: jdh769135520@outlook.com.
  • Yu Ma
    Department of Electronic Engineering, Fudan University, Shanghai, China.
  • Yuanyuan Wang
    Department of Biotechnology, College of Life Science and Technology, Jinan University Guangzhou, 510632, China.

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