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Assess sleep stage by modern signal processing techniques.

Publication ,  Journal Article
Wu, H-T; Talmon, R; Lo, Y-L
Published in: IEEE transactions on bio-medical engineering
April 2015

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.

Duke Scholars

Published In

IEEE transactions on bio-medical engineering

DOI

EISSN

1558-2531

ISSN

0018-9294

Publication Date

April 2015

Volume

62

Issue

4

Start / End Page

1159 / 1168

Related Subject Headings

  • Sleep Stages
  • Signal Processing, Computer-Assisted
  • Middle Aged
  • Male
  • Humans
  • Female
  • Electroencephalography
  • Biomedical Engineering
  • Algorithms
  • Adult
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Wu, H.-T., Talmon, R., & Lo, Y.-L. (2015). Assess sleep stage by modern signal processing techniques. IEEE Transactions on Bio-Medical Engineering, 62(4), 1159–1168. https://doi.org/10.1109/tbme.2014.2375292
Wu, Hau-Tieng, Ronen Talmon, and Yu-Lun Lo. “Assess sleep stage by modern signal processing techniques.IEEE Transactions on Bio-Medical Engineering 62, no. 4 (April 2015): 1159–68. https://doi.org/10.1109/tbme.2014.2375292.
Wu H-T, Talmon R, Lo Y-L. Assess sleep stage by modern signal processing techniques. IEEE transactions on bio-medical engineering. 2015 Apr;62(4):1159–68.
Wu, Hau-Tieng, et al. “Assess sleep stage by modern signal processing techniques.IEEE Transactions on Bio-Medical Engineering, vol. 62, no. 4, Apr. 2015, pp. 1159–68. Epmc, doi:10.1109/tbme.2014.2375292.
Wu H-T, Talmon R, Lo Y-L. Assess sleep stage by modern signal processing techniques. IEEE transactions on bio-medical engineering. 2015 Apr;62(4):1159–1168.

Published In

IEEE transactions on bio-medical engineering

DOI

EISSN

1558-2531

ISSN

0018-9294

Publication Date

April 2015

Volume

62

Issue

4

Start / End Page

1159 / 1168

Related Subject Headings

  • Sleep Stages
  • Signal Processing, Computer-Assisted
  • Middle Aged
  • Male
  • Humans
  • Female
  • Electroencephalography
  • Biomedical Engineering
  • Algorithms
  • Adult