What is multiscale entropy?

Multiscale entropy (MSE) provides insights into the complexity of fluctuations over a range of time scales and is an extension of standard sample entropy measures described here. Like any entropy measure, the goal is to make an assessment of the complexity of a time series.

What is Entropy in EEG?

Entropy measures quantify the uncertainty in the EEG, which roughly equates to the possible configurations or their predictability. However there are many method and parameter choices that can fundamentally change the result and meaning.

What means entropy?

entropy, the measure of a system’s thermal energy per unit temperature that is unavailable for doing useful work. Because work is obtained from ordered molecular motion, the amount of entropy is also a measure of the molecular disorder, or randomness, of a system.

What does a EEG measure?

An EEG is a test that detects abnormalities in your brain waves, or in the electrical activity of your brain. During the procedure, electrodes consisting of small metal discs with thin wires are pasted onto your scalp. The electrodes detect tiny electrical charges that result from the activity of your brain cells.

How is multiscale entropy used in EEG analysis?

In this study, a new approach is introduced using multiscale entropy (MSE) considering the structure information over different time scales.

How are EEG signals used in the brain?

In particular, the methods based on EEG for DOA evaluation have been developed rapidly. The EEG signals which reflect the brain’s activities have been widely used for research and diagnosis, especially for measuring the awareness level of patients.

How is entropy used to monitor depth of anesthesia?

In order to build a reliable index to monitor the depth of anesthesia (DOA), many algorithms have been proposed in recent years, one of which is sample entropy (SampEn), a commonly used and important tool to measure the regularity of data series. However, SampEn only estimates the complexity of signals on one time scale.

How is entropy used in artificial neural networks?

The entropy values over different time scales calculated through MSE are applied as the input data to train an artificial neural network (ANN) model using bispectral index (BIS) or expert assessment of conscious level (EACL) as the target.