*8.2.3 Azathioprine (AZA)*

Azathioprine is especially used in SLE patients with hematological manifestations, but it can also be used in LN, as a maintenance drug [60]. This drug is compatible with pregnancy, since fetal liver lacks the enzyme to convert AZA to its active form [61]. Therefore, AZA is considered a safe drug, but doses should not exceed 2 mg/Kg/day.


**Abstract**

The two main problems in the daily clinical practice of EEG are i) its under-use dedicated mainly to epilepsy and ii) subjectivity in *de visu* analysis. However, both problems can be overcome by using numerical tools in clinical practice that broaden the scope and introduce real objectivity to bioelectrical measurements. We have developed a method for quantitative EEG (qEEG) for daily use based on the homeostatic foundation of EEG. This method is robust, easy, and not time consuming and is arranged in two branches: the analysis of the spectral composition in each channel and synchronization. Notably, channels are arranged in differential mode. Since 2016, we have used this method for more than 4100 EEGs from scalp recordings in outpatients, epilepsy evaluation, and evaluation and monitoring in the intensive care unit (ICU). We have been able to identify numerical properties that are not visually evident in several pathologies, including COVID-19 in patients suffering encephalopathy, and have performed diagnosis in ICU patients and differentiation between epileptic and non-epileptic spells or minimum cognitive states. The use of numerical variables across successive recordings in the same patient has proven to be of great utility. We propose that qEEG use should be expanded globally for daily clinical practice.

**Keywords:** encephalopathy, epilepsy, fast Fourier transform, numerical methods, psychogenic non-epileptic seizures, spectral entropy, synchronization

## **1. Introduction**

Electroencephalography (EEG) is one of the oldest diagnostic methods currently used in medicine. It was described one century ago by the German psychiatrist Hans Berger [1]. Since then, its use has rapidly spread, and practically every hospital in the world has an EEG device. However, although EEG is probably the fastest, cheapest, and most straightforward method to obtain neurophysiological information from the human brain in a non-invasive way [2], its use is sometimes excessively restricted to the diagnosis of epilepsy, even in patients in whom the level of consciousness should be carefully evaluated [3]. Nevertheless, it should be always remembered that the primary function of the cerebral cortex is to exchange information by generating bioelectrical signals, not only in epilepsy.

In addition to this excessive restriction to epilepsy, EEG is sometimes reported as a subjective method, depending strongly on the interpreter [4–8]. There has been an attempt to reduce inter-rater variability among interpreters by the introduction of a consensus for EEG interpretation [9–13]. Although these consensus and

classifications introduce some objectivity in EEG analysis, the variables are mostly qualitative or binary (present/absent), and specificity is therefore not very high.

In the past few decades, clinicians, neuroscientists, mathematicians, physicists, and engineers (among other experts) have sought a way to overcome these flaws to increase the true field of EEG application and reduce subjectivity in diagnosis. Some research has been devoted to combining EEGs and imaging techniques, mainly magnetic resonance imaging [14–16]. Another direction has been to develop mathematical tools to increase the reliability and deepen the information extracted from neurophysiological recordings. Collectively, this approach is called quantitative EEG (qEEG [17]). A canonical formulation toolbox is not needed to define qEEG. Instead, we can say that every EEG recording that uses any kind of post hoc numerical method to obtain a result about frequency spectrum, synchronization, network dynamics, or anything else of bioelectrical magnitude can be considered qEEG. This approach has been increasing in popularity since the introduction of digitalization in electroencephalography. As an example, in the past few decades, the number of papers referenced in PubMed with the word "qEEG" in the title/ abstract rose from two in 1979, to 31 in 1999, and 76 in 2019 (a factor of x38!). However, the use of qEEG in clinical practice is far from being generalized, with the exception of ICUs where long-term monitoring (continuous EEG, cEEG) and qEEG are slowly increasing [18–25].

The time required for a cEEG review is one of the most commonly given reasons for the use of qEEG in the ICU and other diagnostic fields. However, we have taken a different approach to qEEG during cEEG or standard EEG for ambulatory/hospitalized patients: instead of just simplifying seizure detection (another manifestation of the excessive focus on epilepsy), our aim is to obtain a comprehensive and efficient view of the bioelectrical brain physiology/physiopathology in the most objective way. To do this, we have developed qEEG using classical mathematical methods, but in a neurophysiologically and clinically oriented fashion [26].

In this chapter, we want to describe in detail the physiological basis of qEEGs, the method implemented for its quantification, and provide some examples of its use.