Approximate entropy (ApEn) is a recently formulated family of parameters and statistics
quantifying regularity (orderliness) in serial data, with developments within theoretical …

Characteristic patterns of variation over time, namely rhythms, represent a defining feature of
complex systems, one that is synonymous with life. Despite the intrinsic dynamic …

Highlights:► Twelve entropy indices were systematically compared in monitoring depth of
anesthesia and detecting burst suppression.► Renyi permutation entropy performed best in …

Emerging neural theories of consciousness suggest a correlation between a specific type of
neural dynamical complexity and the level of consciousness: When awake and aware …

Fuzzy entropy (FuzzyEn), a new measure of time series regularity, was proposed and
applied to the characterization of surface electromyography (EMG) signals. Similar to the two …

The EEG (Electroencephalogram) signal indicates the electrical activity of the brain. They
are highly random in nature and may contain useful information about the brain state …

This paper compares three related measures of complexity, ApEn, SampEn, and FuzzyEn.
Since vectors' similarity is defined on the basis of the hard and sensitive boundary of …

The electroencephalogram (EEG) signal plays an important role in the diagnosis of epilepsy.
The EEG recordings of the ambulatory recording systems generate very lengthy data and …

THERE are a number of concepts and analytical techniques directed to quantifying the
irregularity of stochastic signals, such as the EEG. One such concept is entropy. Entropy …

A new approach for quantifying the relationship between brain activity patterns and depth of
anesthesia (DOA) is presented by analyzing the spatio-temporal patterns in the …