Assess sleep stage by modern signal processing techniques.

Published

Journal Article

In this paper, two modern adaptive signal processing techniques, empirical intrinsic geometry and synchrosqueezing transform, are applied to quantify different dynamical features of the respiratory and electroencephalographic signals. We show that the proposed features are theoretically rigorously supported, as well as capture the sleep information hidden inside the signals. The features are used as input to multiclass support vector machines with the radial basis function to automatically classify sleep stages. The effectiveness of the classification based on the proposed features is shown to be comparable to human expert classification-the proposed classification of awake, REM, N1, N2, and N3 sleeping stages based on the respiratory signal (resp. respiratory and EEG signals) has the overall accuracy 81.7% (resp. 89.3%) in the relatively normal subject group. In addition, by examining the combination of the respiratory signal with the electroencephalographic signal, we conclude that the respiratory signal consists of ample sleep information, which supplements to the information stored in the electroencephalographic signal.

Full Text

Duke Authors

Cited Authors

  • Wu, H-T; Talmon, R; Lo, Y-L

Published Date

  • April 2015

Published In

Volume / Issue

  • 62 / 4

Start / End Page

  • 1159 - 1168

PubMed ID

  • 25438301

Pubmed Central ID

  • 25438301

Electronic International Standard Serial Number (EISSN)

  • 1558-2531

International Standard Serial Number (ISSN)

  • 0018-9294

Digital Object Identifier (DOI)

  • 10.1109/tbme.2014.2375292

Language

  • eng