*3.2.9. Auditory evoked potentials*

The following formula is used to calculate approximate entropy:

Cm(r) \_\_\_\_\_\_ Cm+1

Applied to time series, approximate entropy is a measurement of series predictability. As we know, electroencephalographic signal is a time variation of scalp-recorded potential. Thus, electroencephalographic signal may be described as a time series. Calculating approximate entropy, there results an estimation of EEG signal predictability, and, inherently, an estimation of the signal complexity. The more awake the patient is, the higher values the approximate entropy will have, as the EEG is more complex and less predictable. During deep sedation, EEG complexity lowers and thus will be more predictable, with a lower approximate entropy value. Approximate value is used to estimate anesthesia depth and correlates well with BIS and SEF

Permutation entropy is another method of estimating the chaos, which analyzes the probability of appearance of a motive of amplitude over a certain amount of time. The more motifs there are, the more complex the signal is, therefore the more awake the patient is. When the probability of appearance of all motifs is equal, permutation entropy equals 1. The calculation algorithm for the permutation entropy was published in 2002 by Bandt, and in 2008, Jordan

<sup>∑</sup>Pi <sup>×</sup> ln <sup>P</sup> \_\_\_\_\_\_\_\_i

An important parameter is the signal acquisition frequency, the algorithm being designed for

In 2008, Olofsen et al. studied EEG by using permutation entropy during propofol anesthesia

Using permutation entropy, the transition between loss of consciousness and consciousness

Fractal analysis of the EEG signal implies measuring the degree of self-similarity of the signal.

EEG fractal analysis was used to study sleep, anesthesia or convulsions [61–63].

(r)) (5)

lnN (6)

ApEn(Sn, <sup>m</sup>, r) <sup>=</sup> ln(

indices, during propofol-remifentanil anesthesia [56].

PE = −

where P = probability of appearance of a motif,

et al. use this algorithm to study electroencephalograms [57, 58].

and described six types of motifs: peaks, slopes and grooves [59].

can be detected by analyzing 2-seconds EEG recordings [60].

Cm(r) = prevalence of repetitive patterns, with the length m.

where m = length of the pattern,

90 Current Topics in Intensive Care Medicine

*3.2.7. Permutation entropy*

N = number of motifs.

*3.2.8. EEG fractality*

a frequency of 100 or 128 Hz.

Changes in the latency and amplitude of auditory evoked potentials of middle latency (early cortical), that appear 20–80 ms after auditory stimulation, can be correlated with anesthetic depth [65–67].

The auditory evoked potential index (AAI) is an algorithm integrating amplitude variations of several consecutive potentials and generating a numerical outcome, between 0 and 99, similar to the bispectral index [68]. Patients lose consciousness under 40, and surgical anesthesia appears under 20. AAI values are well correlated with BIS values [69]. In the ICU, middle latency evoked potentials have a positive prognostic value in the patients who required craniotomy for TBI, and there has been noticed a strong correlation between pupillary responses, intracranial pressure and auditory evoked potentials in patients with supratentorial mass lesions [70, 71].
