IJRE – Volume 5 Issue 4 Paper 1


Author’s Name :  Chetana Mohan Jadhav | Prof V G Puranik

Volume 05 Issue 04  Year 2018  ISSN No:  2349-252X  Page no: 1- 3






In human beings, sleep is a universal recurring dynamical and physiological activity, and the quality of sleep influences our daily lives in diverse ways. In this project we are proposing modern adaptive signal processing techniques, empirical intrinsic geometry and synchro squeezing transform, are applied to quantify different dynamical features of the respiratory and electroencephalographic signals. We will show that the proposed features will theoretically rigorously support, as well as capture the sleep information hidden inside the signals. The features can be used as input to multiclass support vector machines with the radial basis function to automatically classify sleep stages.


Sleep Stage, Synchrosqueezing Transform (ST), SVM Classifier, EEG Signal


  1. Panagiotis C. Petrantonakis, Leontios J. Hadjileontiadis, “Adaptive Emotional Information Retrieval from EEG Signals in the Time-Frequency Domain”, IEEE (2012).
  2. Anna Maria Bianchi, “Processing of Signals Recorded Through Smart Devices: Sleep-Quality Assessment”, IEEE (2010).
  3. Sinan Kaplan, Mehmet Amac Guvensan “Driver Behavior Analysis for Safe Driving: A Survey”, IEEE (2015).
  4. T. Lee-Chiong, Sleep Medicine: Essentials and Review. London, U.K.: Oxford Univ. Press, 2008.
  5. V. Bajaj and R. B. Pachori, “Automatic classification of sleep stages based on the time-frequency image of EEG signals,” Comput. Methods Programs Biomed., vol. 112, no. 3, pp. 320–328, 2013.
  6. S. Geng et al., “EEG non-linear feature extraction using correlation dimension and hurst exponent,” Neurological Res., vol. 33, no. 9, pp. 908–912, 2011.
  7. I. Daubechies et al., “Synchro squeezed wavelet transforms: An empirical mode decomposition-like tool,” Appl. Comput. Harmon. Anal., vol. 30, pp. 243–261, 2011.
  8. D. Duncan et al., “Identifying preseizure state in intracranial EEG data using diffusion kernels,” Math. Biosci. Eng., vol. 10, pp. 579–590, 2013.
  9. G. S. Chung et al., “REM sleep classification with respiration rates,” in Proc. 6th Int. Special Topic Conf. Inf. Technol. Appl. Biomed., 2007, pp. 194–197.