Entropy is a powerful tool for the analysis of time series, as it allows describing the
probability distributions of the possible state of a system, and therefore the information …

Over the last years, a surge of empirical studies converged on complexity-related measures
as reliable markers of consciousness across many different conditions, such as sleep …

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

Slope entropy (SlEn) is a time series complexity indicator proposed in recent years, which
has shown excellent performance in the fields of medical and hydroacoustics. In order to …

In order to accurately identify various types of ships and develop coastal defenses, a single
feature extraction method based on slope entropy (SlEn) and a double feature extraction …

Bearing fault diagnosis has attracted significant attention over the past few decades. It
consists of two major parts: vibration signal feature extraction and condition classification for …

Background The neural correlates of anesthetic-induced unconsciousness have yet to be
fully elucidated. Sedative and anesthetic states induced by propofol have been studied …

Complexity may be one of the most important measurements for analysing time series data;
it covers or is at least closely related to different data characteristics within nonlinear system …

Quantification of complexity in neurophysiological signals has been studied using different
methods, especially those from information or dynamical system theory. These studies have …

Recently, neuroimaging technologies have been developed as important methods for
assessing the brain condition of patients with disorders of consciousness (DOC). Among …