Mild cognitive impairment (MCI) can be an indicator representing the early stage of
Alzheimier's disease (AD). AD, which is the most common form of dementia, is a major …

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

The development of new measures and algorithms to quantify the entropy or related
concepts of a data series is a continuous effort that has brought many innovations in this …

Automatic detection of epileptic seizure from brain signal data (eg electroencephalogram
(EEG)) is very crucial due to dynamic and complex nature of EEG signal (eg non …

The electroencephalogram (EEG) can reflect brain activity and contains abundant
information of different anesthetic states of the brain. It has been widely used for monitoring …

In this work, we propose a novel method to transform a time series into a weighted and
directed network. For a given time series, we first generate a set of segments via a sliding …

In recent decades, entropy measures have gained prominence in neuroscience due to the
nonlinear behaviour exhibited by neural systems. This rationale justifies the application of …

Permutation Entropy (PE) is a cost effective tool for summarizing the complexity of a time
series. It has been used in many applications including damage detection, disease …

Two main weaknesses have been identified for permutation entropy (PE): the neglect of
subsequence pattern differences in terms of amplitude and the possible ambiguities …

Permutation entropy (PE) has been widely exploited to measure the complexity of the
electroencephalogram (EEG), especially when complexity is linked to diagnostic information …