Continuous time wavelet entropy of auditory evoked potentials

Abstract

a b s t r a c tIn this paper, the continuous time wavelet entropy (CTWE) of auditory evoked potentials (AEP) has been characterized by evaluating the relative wavelet energies (RWE) in specified EEG frequency bands. Thus, the rapid variations of CTWE due to the auditory stimulation could be detected in post-stimulus time interval. This approach removes the probability of missing the information hidden in short time intervals. The discrete time and continuous time wavelet based wavelet entropy variations were compared on non-target and target AEP data. It was observed that CTWE can also be an alternative method to analyze entropy as a function of time.

Discrete Wavelet Entropy Aided Detection of Abrupt Change: A Case Study in the Haihe River Basin, China

“…There are obvious differences between stable systems and changing systems. Generally speaking, the complexity of a system is relevant to its entropy in the way that the entropy increases with the degree of disorder: the larger the entropy, the more disorder and complex a system [11,33,40,41]. Here we assume that a climatic system under natural condition is stable, while it would become disordered and complex when encountering climate change.…”

“…The wavelet analysis (WA) method is capable of elucidating the localized characteristics of non-stationary series both in temporal and frequency domains [29][30][31]. The wavelet entropy, combined by WA and information entropy theories, is an important concept of describing the variability and complexity of dynamic systems with non-stationary and multi-temporal characteristics [32,33]. It is to first analyze a series by WA, such as continuous or discrete wavelet transform, and then calculate the entropy measures such as Shannon entropy [34], mutual information and relative entropy [35].…”

Wavelet-Based Analysis on the Complexity of Hydrologic Series Data under Multi-Temporal Scales

“…In the authors' opinion, this conclusion makes sense from the physical point of view. Because the Type-II pdf is estimated based on the energy distribution (both the characteristics and composition) of series data [22], by using it the complexity of hydrologic series data under multi-temporal scales can be quantified more accurately and reliably.…”

“…Furthermore, two approaches are used to estimate the pdf; one is the interval-sampling based on the assumption of series' random characters [11], and the other is based on the wavelet energy distribution of series data [22]; they are called the Type-I pdf and Type-II pdf respectively for easily stating contents. The latter is described as follows.…”

“…The wavelet entropy (WE), which is the combination of WA with information theory, is an important and commonly used concept of describing the complexity of hydrologic series under multi-temporal scales [21,22]. Concretely, it is used first to analyze hydrologic series data by WA, such as the continuous wavelet transform (CWT) and multi-resolution analysis (MRA), and then to calculate the entropy measures, mainly including the conventional Shannon entropy [13], mutual information content [23] and relative entropy [24].…”