Abstract: 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.
Abstract: This paper introduces a new instantaneous frequency
computation approach -Counting Instantaneous Frequency for a
general class of signals called simple waves. The classsimple wave
contains a wide range of continuous signals for which the concept
instantaneous frequency has a perfect physical sense. The concept of
-Counting Instantaneous Frequency also applies to all the discrete data.
For all the simple wave signals and the discrete data, -Counting
instantaneous frequency can be computed directly without signal
decomposition process. The intrinsic mode functions obtained through
empirical mode decomposition belongs to simple wave. So
-Counting instantaneous frequency can be used together with
empirical mode decomposition.