Adaptive Subscale Entropy Based Quantification of EEG

Young-Seok CHOI  

IEICE TRANSACTIONS on Information and Systems   Vol.E97-D   No.5   pp.1398-1401
Publication Date: 2014/05/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E97.D.1398
Type of Manuscript: LETTER
Category: Biological Engineering
EEG,  adaptive subscale entropy,  empirical mode decomposition,  intrinsic mode function,  

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This letter presents a new entropy measure for electroencephalograms (EEGs), which reflects the underlying dynamics of EEG over multiple time scales. The motivation behind this study is that neurological signals such as EEG possess distinct dynamics over different spectral modes. To deal with the nonlinear and nonstationary nature of EEG, the recently developed empirical mode decomposition (EMD) is incorporated, allowing an EEG to be decomposed into its inherent spectral components, referred to as intrinsic mode functions (IMFs). By calculating Shannon entropy of IMFs in a time-dependent manner and summing them over adaptive multiple scales, the result is an adaptive subscale entropy measure of EEG. Simulation and experimental results show that the proposed entropy properly reveals the dynamical changes over multiple scales.