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 classificationthe 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. Index Terms-Breathing pattern variability, empirical intrinsic geometry (EIG), sleep stage, synchrosqueezing transform (ST). 0018-9294
EIG has also been applied to data including both respiratory and EEG signals REF .
Assess Sleep Stage by Modern Signal Processing Techniques
IEEE Transactions on Biomedical Engineering
IEEE Transactions on Biomedical Engineering
Physics Mathematics Computer Science Medicine