*3.2.5. Consciousness index*

The monitor for the consciousness index is a wireless, portable monitor as well, with a 10-meters range. It is produced by Morpheus Medical. It provides a score with values between 0 and 100, and, similar to the BIS monitoring during anesthesia, the value of the consciousness index must be maintained between 40 and 60 to prevent waking up during anesthesia. This monitor analyzes EEG, using symbolic dynamic analysis. As EEG is a variation of potential through time, it can be seen as a dynamic system, in which every moment has a state that can be defined through a real number. The dynamic symbol method analyzes a dynamic system as being composed of a discrete sequence of abstract symbols that each correspond to a system state [52].

This monitor was compared with BIS monitoring and similar results were found [53].

There is one other consciousness index that uses Lempel-Ziv complexity analysis. This method was established in 2013 by a team of researchers led by Adenauer Casali and Olivia Gosseries. This index was studied during midazolam sedation and propofol-xenon anesthesia, on a limited number of subjects. It is based on evaluating cortical reactivity and intercortical connectivity, using high-density EEG and transcranial magnetic stimulation on several cortical areas: superior occipital gyrus, superior medial frontal gyrus, superior parietal gyrus and premotor rostral cortex. EEG signals were analyzed using the Lempel-Ziv complexity algorithm, which approximates the amount of nonredundant information in a binary system, thus estimating the minimal amount of patterns required to describe a signal. The less EEG signal nonredundant information there is, the less complex the signal and deep the anesthesia is [54].

#### *3.2.6. Approximate entropy*

Entropy is the degree of disorder in a system, thus an extensive measurement of chaos. At the beginning of the twentieth century, the mathematicians Andrey Kolmogorov and Henri Poincare further developed the mathematical analysis of chaos. In 1991, Steven Pincus introduced the notion of approximate entropy. Approximate entropy measures the complexity of a system. As it is little influenced by noise, it has an advantage in the analysis of systems exposed to a strong source of noise. Mathematically, approximate entropy quantifies how constant the distance between two vectors in a series is [55].

The following formula is used to calculate approximate entropy:

$$\text{ApEn(Sn, m, r)} = \ln\left(\frac{\mathbb{C}\_{\text{m}}(\text{t})}{\mathbb{C}\_{\text{m} \circ l}(\text{t})}\right) \tag{5}$$

Another analysis parameter for complexity, similar with fractal analysis, is detrended fluctuation analysis (DFA). It is an analysis method for signal self-similarity and was used to evaluate EEG and was suggested as a possible quantification parameter of anesthesia depth [64].

Important Issues in Coma and Neuromonitoring http://dx.doi.org/10.5772/intechopen.79448 91

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

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].

Jobsis first noticed in 1977 [72] that tissues are transparent for a wavelength of light of 700–950 nm. Based on this, the concentration of oxyhemoglobin, deoxyhemoglobin and cyto-

Starting from the oxyHb and deoxyHb concentrations, one can estimate regional saturation

by evaluating the changes of total hemoglobin (HbT). Monitors for cerebral oxygenation, that are based on the NIRS technology, use a sensor placed above the tissue, whose oxygenation is to be measured. The sensor is made of emitting and detecting diodes, placed within 4–8 cm of each other. Detecting diodes will detect the infrared light reflected by the tissue. In the case of cerebral tissue, the infrared light can penetrate up to a depth of 0.6–1 centimeters [73]. Thus, cerebral oxygenation through this method is underestimated, compared with jugular vein

tion because 70% of cerebral blood is in the veins and capillaries, and thus, normal cerebral

Additionally, NIRS values have prognostic value in TBI patients. The values of rSO<sup>2</sup> at hospital admission were 74.7 ± 1.5% in the case of surviving patients and 61.9 ± 19.4%

specificity of 78.6% over CT scans in detecting intracranial hematoma.

 values are between 60 and 80%. Using NIRS in the current clinical practice began in the 1980s, with the first studies on monitoring cerebral function in the adult and neonate. More recent studies are focused upon evaluating prehospital coma gravity. For example, Peters et al. [75] observed in a study including 25 patients that NIRS has a sensitivity of 93.3% and a

) in a tissue. Furthermore, the regional changes of blood flow can be assessed,

) [74]. Among the benefits of this method are the noninvasive character and

values are closer to the venous saturation than to arterial satura-

chrome C oxidase can be measured (only the first two are used in clinical practice).

*3.2.9. Auditory evoked potentials*

**4. Near-infrared spectroscopy (NIRS)**

depth [65–67].

of oxygen (rSO<sup>2</sup>

saturation (SjVO<sup>2</sup>

rSO<sup>2</sup>

the ease of use at the bedside.

In the case of the brain, rSO<sup>2</sup>

where m = length of the pattern,

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

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 indices, during propofol-remifentanil anesthesia [56].

#### *3.2.7. Permutation entropy*

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

$$\text{PE} = -\frac{\sum P\_i \times \ln P\_i}{\ln \text{N}} \tag{6}$$

where P = probability of appearance of a motif,

N = number of motifs.

An important parameter is the signal acquisition frequency, the algorithm being designed for a frequency of 100 or 128 Hz.

In 2008, Olofsen et al. studied EEG by using permutation entropy during propofol anesthesia and described six types of motifs: peaks, slopes and grooves [59].

Using permutation entropy, the transition between loss of consciousness and consciousness can be detected by analyzing 2-seconds EEG recordings [60].
