TY - JFULL AU - Young-Seok Choi PY - 2015/6/ TI - Empirical Mode Decomposition Based Multiscale Analysis of Physiological Signal T2 - International Journal of Electrical and Computer Engineering SP - 1315 EP - 1319 VL - 9 SN - 1307-6892 UR - https://publications.waset.org/pdf/10001603 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 101, 2015 N2 - We present a refined multiscale Shannon entropy for analyzing electroencephalogram (EEG), which reflects the underlying dynamics of EEG over multiple scales. The rationale behind this method 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 a decomposition of EEG into its inherent spectral components, referred to as intrinsic mode functions (IMFs). By calculating the Shannon entropy of IMFs in a time-dependent manner and summing them over adaptive multiple scales, it results in an adaptive subscale entropy measure of EEG. Simulation and experimental results show that the proposed entropy properly reveals the dynamical changes over multiple scales. ER -