MODERN SIGNAL PROCESSING TECHNIQUES FOR EEG SIGNALS TO DETECT SLEEP STAGES
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
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