Detection of Spindles in Sleep EEGs Using a Novel Algorithm Based on the Hilbert-Huang Transform
Zhihua Yang, Lihua Yang, Dongxu Qi · Birkhäuser Basel eBooks · 2007
A novel approach for detecting spindles from sleep EEGs (electroencephalograph) automatically is presented in this paper. Empirical mode decomposition (EMD) is employed to decompose a sleep EEG, which are usually typical nonlinear and non-stationary data, into a finite number of intrinsic mode functions (IMF). Based on these IMFs, the Hilbert spectrum of the EEG can be calculated easily and provides a high resolution time-frequency presentation. An algorithm is developed to detect spindles from a sleep EEG accurately, experiments of which show encouraging detection results.