**5. Some examples of the utility of qEEG**

It is out of the scope of this chapter to illustrate the objective and quantified difference between the classical, *de visu*, analysis of EEG and qEEG. Instead, we will provide some examples of qEEG application to real clinical problems and the results. In these examples it will be shown that information obtained by qEEG clearly exceeds the possibility of *de visu* analysis.

#### **5.1 Differentiation between periodic patterns and seizures**

Patients in the ICU usually undergo a limited clinical neurological examination because of either structurally or functionally altered conditions of the central

nervous system (CNS) or due to the effects of drugs used for sedoanalgesia [36]. Therefore, we can evaluate brain function in these conditions by EEG. However, a dynamic evolution of injury is frequently observed in critically ill patients due to the occurrence of epileptic seizures (ES), status epilepticus (SE), or other brain insults [37, 38]. In this sense, cEEG allows the functional assessment of the cerebral cortex in real time for prolonged periods. It has been proven to be an extraordinarily useful tool for detecting electrographic seizures and non-convulsive epileptic status (NCES), modifying treatment and assessing the functional prognosis [10, 11, 39–42].

The particularity of cEEG records of patients in the ICU comprises a high frequency of artifacts and frequently observed rhythmic and periodic patterns, which are easily confused with seizures. Both can be difficult to interpret not only in a raw EEG but also with the qEEG tools currently used [43, 44]. As indicated above, the use of multiple drugs acting on the CNS and primary and secondary injuries profoundly affect the bioelectrical brain dynamics. Therefore, it can be quite difficult to differentiate between bursts of periodic activity (BPA) and true ES/SE. The main problem is that EEG patterns analyzed *de visu* do not always exhibit the sharp morphology of ES/SE of not-ICU patients.

According to the ILAE, an ES is a transient occurrence of signs and/or symptoms due to abnormal excessive or synchronous neuronal activity in the cortex of the brain [45]. Signs/symptoms are usually excluded in critically ill patients, but the excessive activity of the cortex is mandatory for a positive identification. We have used this pathophysiological feature for the numerical definition of ES. Therefore, we can use this method to exclude epilepsy in those cases where α/β activity does not change (or even decreases) during the event (see below). The limits of change for the different bands (**Table 1**) can be used to distinguish PBA from ES.

From this table we can observe that the superposition is very high for δ bands (i.e., this band is not discriminative), low for θ and *Se,* and practically null for α and β bands. Therefore, the intervals for increments of normalized activity defining an ES in these types of patients are as follows (excluding superposition and rounding): *δ<sup>F</sup>* ¼ ½ � 119, 166 ; *θ<sup>F</sup>* ¼ ½ � 173, 264 ; *θPO* ¼ ½ � 168, 248 ; *θ<sup>T</sup>* ¼ ½ � 151, 274 ; *α<sup>F</sup>* ¼ ½ � 159, 244 ; *αPO* ¼ ½ � 159, 244 ; *α<sup>T</sup>* ¼ ½ � 159, 244 ; *β<sup>F</sup>* ¼ ½ � 141, 374 ; *βPO* ¼ ½ � 146, 262 ; *β<sup>T</sup>* ¼ ½ � 141, 374 ; *SeF* ¼ ½ � 97, 110 ; *SePO* ¼ ½ � 98, 107 ; *SeT* ¼ ½ � 98, 104 .

We have defined the numerical features of ES and BPA in critically ill patients using the pathophysiological definition of epilepsy. This will facilitate its identification in clinical practice, allowing a precocious and more adequate treatment.

#### **5.2 Specific characterization of encephalopathy in SARS-CoV-2 patients**

Neurological complications in COVID-19-infected patients have been reported. The CNS effects reported include encephalitis, toxic encephalopathy, ageusia and anosmia, headaches, or acute cerebrovascular disease [46–52]. The mechanisms of CNS infection in the pathophysiology of COVID-19 are still debated, and it has been proposed to result from direct invasion through the blood–brain barrier, a neuronal pathway, hypoxia damage, immune-response mediated injury, or angiotensinconverter enzyme 2 activity, among other possibilities [47, 53, 54]. Encephalopathy refers clinically to state of impaired cognition, generally acute or subacute [55]. Descriptions *de visu* of EEG are based on classical analysis by visual inspection and not on specific features [56–58].

We applied our method of qEEG to patients discharged from the ICU after COVID-19 infection [34]. We used two control groups from patients previously studied in our hospital: (i) patients with infectious toxic encephalopathy (ENC) and (ii) patients after cardiorespiratory arrest (CRA), as an example of severe hypoxic insult to the CNS.

*Necessity of Quantitative EEG for Daily Clinical Practice DOI: http://dx.doi.org/10.5772/intechopen.94549*


#### **Table 1.**

*Inter-percentile 25–75 intervals for bands and lobes in BPA and ES. Superposition is indicated with respect to the ES interval [26].*

Visually, in COVID-19, EEG records the apparent absence of delta/theta activity conferred a near-physiological aspect to the recordings. Despite the different visual aspects appearing between ENC and COVID-19, the mean spectra by channel were quite similar. Examples of typical recordings are shown in **Figure 3**.

We assessed the specific differences for each band in ENC, COVID-19, and CRA patients. Subsequently, we performed one-way ANOVA (ANOVA on ranks when normality failed) for each lobe and band, and for SSe and synchronization (**Figure 4**).

The pattern of ENC and CRA was clearly different for all bands. However, the COVID-19 group was not completely different from the ENC and CRA groups, although it was evidently observed that the distribution is between the two extreme groups. Nonetheless, the behavior of temporal lobes clearly differs for the ENC and COVID-19 groups for the δ, α, and β bands. Contrary to EEG bands, SSe was higher for the COVID-19 group and lower for the ENC group. In fact, SSe was different for all groups at all lobes. This result may be surprising considering the kind of spectra shown in **Figure 3c**, where the distribution was apparently more complex. However, the presence of α and β bands (scarcely present in the CRA group) increased the SSe for COVID-19 patients.

Finally, although ρ was not as different between groups as SSe, a clear difference was seen in the hemispheric synchronization and frontal lobes of ENC and COVID-19 patients, with lower synchronization for the latter group.

In summary, we have demonstrated that qEEG can differentiate between encephalopathy types, and we have described the numerical features of each. In this context, we show that COVID-19 patients display EEG structures that are truly distinguishable from those of both infectious toxic encephalopathy and encephalopathies of patients who experience severe hypoxic conditions. Significantly, the EEG pattern of COVID-19 patients was between those of the ENC and CRA groups. Therefore, it can be speculated that hypoxia may show some participation in this electroclinical entity. However, the EEG structures of the CRA and COVID-19 groups were different enough to consider that other factors besides hypoxia must be responsible for the bioelectrical pattern.

It is extremely relevant to bear in mind that COVID-19 patients showed mild to severe cognitive symptoms despite *de visu* quasi-normal recordings. However, the severe numerical alterations of temporal lobes spectra, structure of SSe and
