Assess Sleep Stage by Modern Signal Processing Techniques

Hau Tieng Wu, Ronen Talmon, Yu Lun Lo

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Article number6967703
Pages (from-to)1159-1168
Number of pages10
JournalIEEE Transactions on Biomedical Engineering
Issue number4
StatePublished - Apr 1 2015


  • Empirical Intrinsic Geometry (EIG)
  • Sleep Stage
  • Synchrosqueezing transform (ST)
  • breathing pattern variability

ASJC Scopus subject areas

  • Biomedical Engineering


Dive into the research topics of 'Assess Sleep Stage by Modern Signal Processing Techniques'. Together they form a unique fingerprint.

Cite this