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 Table of Contents  
ORIGINAL ARTICLE
Year : 2022  |  Volume : 43  |  Issue : 3  |  Page : 161-168

Evaluation of quantitative electroencephalogram changes in the assessment of anxiety disorders among children and adolescents


1 Department of Pediatrics, Faculty of Medicine, University of Alexandria, Alexandria, Egypt
2 Department of Mental Health, Family Health High Institute of Public Health, Faculty of Medicine, University of Alexandria, Alexandria, Egypt
3 Department of Pediatric, Ministry of Health, Alexandria, Egypt

Date of Submission13-Mar-2022
Date of Decision30-Apr-2022
Date of Acceptance18-Jun-2022
Date of Web Publication16-Dec-2022

Correspondence Address:
MD Shimaa A.M Anwar
Department of Pediatrics, El Nasr Street, Green Plaza, Smouha, Alexandria 21648
Egypt
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/ejpsy.ejpsy_10_22

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  Abstract 


Background Anxiety disorders are considered a major health problem affecting children and adolescents with high incidence and prevalence in different societies.
Aim The present study aimed at detecting the quantitative electroencephalogram (QEEG) changes in children and adolescents with anxiety disorders compared with healthy children. It also aimed to estimate sensitivity and specificity of QEEG in the identification of children with anxiety disorders.
Patients and methods This is a case–control study, which was conducted on 20 children and adolescents with anxiety disorders and 20 healthy children and adolescents. Children were initially screened with the Arabic version of Screen for Child Anxiety Related Disorders and then furtherly subjected to interviewing children and caregivers and finally psychological testing using questionnaires for both the child and parents to verify diagnosis of anxiety disorder according to the Diagnostic and Statistical Manual of Mental Disorders criteria. QEEG recording: QEEG recording was performed to cases and controls under comfortable light and calm room without artifacts to assess spectrum power.
Results Using receiver operating characteristic curve analysis, theta wave spectrum power can significantly detect anxiety disorders in children and adolescents at cutoff less than or equal to 65.4 with a sensitivity and specificity of 80 and 65%, respectively. High-frequency beta wave spectrum power can significantly detect children and adolescents with anxiety disorders at a cutoff more than 23.7 with a sensitivity and specificity of 65 and 90%, respectively.
Conclusion Children and adolescents with anxiety disorders have QEEG changes that coincide with their symptomatology proving that QEEG is a useful method in the assessment and diagnosis of anxiety disorders.

Keywords: alpha wave, beta waves, Diagnostic and Statistical Manual of Mental Disorders, quantitative electroencephalogram, Screen for Child Anxiety Related Disorders, theta wave


How to cite this article:
Abdeldayem HH, Abu-Nazel MW, Sobhy KK, Anwar SA. Evaluation of quantitative electroencephalogram changes in the assessment of anxiety disorders among children and adolescents. Egypt J Psychiatr 2022;43:161-8

How to cite this URL:
Abdeldayem HH, Abu-Nazel MW, Sobhy KK, Anwar SA. Evaluation of quantitative electroencephalogram changes in the assessment of anxiety disorders among children and adolescents. Egypt J Psychiatr [serial online] 2022 [cited 2023 Feb 7];43:161-8. Available from: https://new.ejpsy.eg.net//text.asp?2022/43/3/161/363990




  Introduction Top


Anxiety disorders are considered a serious health problem that affects children and adolescents. Anxiety disorders are said to be one of the most common psychiatric disorders in the childhood age group, with an incidence rate of nearly 5–18% of all children and adolescents (Robert et al., 2019). Its prevalence is between 10 and 30% in the United States with a higher prevalence in females (Ghandour et al., 2019). The prevalence of social anxiety disorder is 7–13% and it may be increased up to 18.75% in adolescents in Egypt (Ragheb et al., 2008). Although the age of onset varies according to the specific disorder, most anxiety disorders are first recognized in late childhood to early adolescent years. Many other psychiatric and medical disorders may be comorbid with anxiety disorders, so these comorbidities severely affect normal daily activities (Melton et al., 2016).

Anxiety is considered a brain response to different stimuli. This brain response is a basic emotion already present in infants and children, so anxiety is not totally pathological as it is an adaptive method in different situations when it helps in avoiding danger. So, it is considered maladaptation when it is frequent, severe, or persistent causing interference with normal daily activities (Beesdo et al., 2009).

Anxiety disorders include generalized anxiety disorder, social anxiety disorder (social phobia), specific phobia, separation anxiety disorder, panic disorder, selective autism, agoraphobia, and post-traumatic stress disorder (American Psychiatric Association, 2013a).

Electroencephalogram (EEG) is used to detect any changes in electrical activity in the brain (Giannakakis et al., 2015). It is widely used in the diagnosis of different neurologic and psychiatric disorders such as epilepsy, learning disorders, autistic spectrum disorders, and attention-deficit hyperactivity disorders (Runyon et al., 2018).

In the last few years, several studies were conducted to determine the role of quantitative electroencephalogram (QEEG), as a new modality in the diagnosis of neuropsychiatric disorders in children including the diagnosis of stroke, dementia, epilepsy, anxiety, and traumatic brain injury (Popa et al., 2020).

Accurate diagnosis and assessment of anxiety disorders in children and adolescents is very important for both treatment and research (Acharya et al., 2018).

In this study, we aimed at detecting QEEG changes in children and adolescents with anxiety disorders compared with healthy children. Also the study aimed to estimate sensitivity and specificity of QEEG in the identification of children with anxiety disorders.


  Patients and methods Top


The sample size was divided into two groups. Patients were 40 children and adolescents, group 1 (cases): 20 children and adolescents with anxiety disorder. Group 2 (controls): 20 matched healthy children and adolescents. The cases were recruited from Child Psychiatry and Neurology Outpatient Clinic at Alexandria University Children’s Hospital. A matched control group for age and sex were selected from the General Paediatric Outpatient Clinics at Alexandria University Children’s Hospital.

Inclusion criteria of cases

Children and adolescents diagnosed with anxiety disorder according to Diagnostic and Statistical Manual of Mental Disorders criteria.

Inclusion criteria of the control groups

Healthy children and adolescents matched for age and sex.

Exclusion criteria of cases

Children diagnosed with mental disorders other than anxiety disorders such as bipolar disorders, autistic spectrum disorder, and depression, children diagnosed with any chronic neurological disease such as epilepsy or attention-deficit hyperactivity disorder and children with chronic non-neurologic diseases such as type I diabetes mellitus or bronchial asthma.


  Methods Top


Recruitment phase

Parents of children attending the study were subjected to a structured interview questionnaire to collect the following data: child demographic data (age and sex) and child medical history of chronic diseases. Children fulfilling study eligibility criteria according to data collected from parents were furtherly subjected to the following: full clinical examination which included general examination (body systems review to exclude chronic disease) with emphasis on neurological examination. Intelligence quotients using Stanford-Binet scale to exclude cases and controls with intelligence quotients less than 80 (Janzen et al., 2004).

The Arabic version of Screen for Child Anxiety Related Disorders (SCARED) (Arab et al., 2016).

The SCARED includes (41 items), each scored as a Likert-type scale of 0–2 (‘not true or hardly ever true,’ ‘somewhat true or sometimes true,’ and ‘very true or often true’). The total score is from 0 to 82.

Case–control study phase

Children initially screened with SCARED were then furtherly subjected to the following:

Full psychiatric evaluation: (Norman et al., 2015) description of the present symptoms, parent and family health information to exclude precipitating factors of anxiety, developmental history of the child, information about school performance, friends and family relationships, interviewing the child or adolescent to evaluate speech, language, intelligence, thinking and emotions, and interviewing parents or guardians and finally psychological assessment using questionnaires for both the child and parents to verify diagnosis of anxiety disorder according to the Diagnostic and Statistical Manual of Mental Disorders criteria (American Psychiatric Association, 2013b). QEEG recording: QEEG recording was performed to cases and controls under comfortable light and calm room without artifacts to assess spectrum power. Nineteen electrode caps according to the 10–20 international QEEG configuration to measure absolute power of each band in all areas. Gel was applied to each electrode site (parietal, frontal, temporal, occipital, and central) (Thakor and Tong, 2004). Analysis: QEEG spectral power is estimated by separating the EEG recordings using computerized algorithms including the fast Fourier transform [this is an algorithm that computes the discrete Fourier transform of a sequence; so, its analysis converts a signal (either a space or time) to a frequency domain] into activities within narrow frequency bands.

QEEG was recorded for each patient. Power of channel and frequency bands including delta (1 to <4 Hz), theta (4 to <8 Hz), low beta (12 to <20 Hz), high beta (20 to <30), and alpha (8 to <12 Hz) with frontal, temporal, central, and occipital areas were recorded. Finally, all the EEG data were analyzed to reject artifacts as eye movements, blinking, or muscle activity (Sterman and Kaiser, 2008).

The software provides mathematical processing of the received data. The settings were set on low-pass filters of 35 Hz, high-pass filters of 5 Hz, and a 50 Hz notch filter (Jeste et al., 2015).

Ethical considerations

The study protocol was approved by the Ethics committee of Faculty of Medicine, University of Alexandria. The caregivers were asked to provide written consents for their children to take part in the study, after explaining the purpose of the study. All data and information from the participants were kept confidential.

IRB NO (Institutional Review Board Number): 00012098.


  Results Top


As regards the delta wave spectrum power, the two study groups showed a P value of 0.71, 0.62, 0.31, 0.20, and 0.75 in the areas of right temporal (anger), left frontal polar (irritability), right temporal (emotion content), right parietal (personality), and right frontal polar (emotion inhibition), respectively. This was statistically not significant ([Table 1]).
Table 1 Comparison between the two study groups regarding delta spectrum power

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Concerning the delta wave amplitude, the anxiety group showed a significantly higher amplitude of left frontal polar (irritability) compared with the control group (P=0.031). There was no statistically significant difference between the two study groups in right temporal (anger), right temporal (emotion content), right parietal (personality), and right frontal polar (emotions inhibition) categories in delta amplitude (P>0.05) ([Table 2]).
Table 2 Comparison between the study groups regarding delta amplitude

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There was a statistically significant difference regarding the theta spectrum power. The anxiety group showed a significant low power of right temporal (anger) (P=0.007), right temporal (emotion content) (P=0.032), and right parietal (personality) (P=0.01) categories compared with the control group. However, there was no statistically significant difference between the two study groups in left frontal polar (irritability) (P=0.166) and right frontal polar areas (emotions inhibition) (P=0.102) ([Table 3]).
Table 3 Comparison between the study groups regarding theta spectrum power

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The theta wave amplitude in the anxiety group showed a significant lower amplitude of right temporal (anger) (P=0.004), left frontal polar (irritability) (P=0.03), right temporal (emotion content) (P=0.017), and right parietal (personality) (P=0.007) categories compared with the controls. However, there was no statistically significant difference between the two study groups in right frontal polar (emotions inhibition) (P=0.05) ([Figure 1]).
Figure 1 Boxplot showing comparison between study groups regarding anger amplitude.

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Concerning the alpha spectrum power, there was no statistically significant difference between the two study groups in right temporal (anger), right temporal (emotion content), right parietal (personality), right frontal polar (emotions inhibition) categories, and left frontal polar (irritability) (P=0.414, 0.148, 0.140, 0.358, 0.75, and 0.683, respectively) ([Table 4]).
Table 4 Comparison between the study groups regarding alpha wave spectrum power

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High-frequency (HF) beta wave spectrum power showed significantly higher power of right temporal (anger) and right parietal (personality) categories in children and adolescents with anxiety compared with controls (P=0.037 and 0.038, respectively). Nevertheless, there was no statistically significant difference between the two study groups in left frontal polar (irritability) (P=0.103), right temporal (emotion content) (P=0.08), and right frontal polar (emotions inhibition) (P=0.296) ([Table 5]).
Table 5 Comparison between the study groups regarding the power of high-frequency Beta wave

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There was a statistically significant difference regarding HF beta amplitude in the anxiety group compared with the controls. The cases showed significantly higher amplitude of right temporal (anger) (P=0.004), left frontal polar (irritability) (P=0.016), right temporal (emotion content) (P=0.007), and personality (P=0.004) categories compared with the control group. However, there was no significant statistical difference between the study groups in right frontal polar (emotions inhibition) (P=0.296) ([Figure 2]).
Figure 2 Boxplot showing comparison between study groups regarding HF amplitude/personality. HF, high frequency.

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Concerning the low-frequency (LF) beta amplitude, the anxiety group showed a significantly higher amplitude of right temporal (anger), right temporal (emotion content), and personality with P value of 0.008, 0.015, and 0.012, respectively, compared with the control group. However, there was no statistically significant difference between the two study groups in left frontal polar (irritability) (P=0.074) and right frontal polar (emotion inhibition) (P=0.195) ([Figure 3]) regions.
Figure 3 Boxplot showing comparison between study groups regarding LF amplitude/personality. LF, low frequency.

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There was a strong correlation between HF beta spectrum power and anxiety symptoms. The receiver operating characteristic curve analysis for HF beta wave was found to be accurate in discriminating between children and adolescents with and without anxiety disorders with a sensitivity and specificity of 55 and 90%, respectively (area under the curve=0.771, P=0.001) ([Table 6], [Figure 4]).
Table 6 High-frequency beta wave validity

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Figure 4 Receiver operating characteristic curve of theta wave for the detection of children and adolescents with anxiety disorders.

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  Discussion Top


Many studies have reported the value of QEEG as a new tool for the assessment of anxiety disorders (Arikan et al., 2006; Jones and Hitsman, 2018; Gregory et al., 2020).

The present study highlighted the significance of studying QEEG changes in anxiety disorders in this group of children and adolescents.

In the present study, we recorded spectrum power and amplitude changes of delta, theta, alpha, LF beta, and HF beta waves.

Regarding theta wave, cases showed statistically significant lower values than the control group according to spectrum power in right temporal and right parietal regions.

The explanation of low values of theta waves spectrum power is that theta waves (>4–8 Hz) are related to early sleep or when preparing to sleep and so, low values of spectrum power of theta waves mean less relaxation and more irritability (Ribas et al., 2018).

The HF beta spectrum power wave was significantly higher in cases than controls in right temporal (anger) and right parietal (personality) regions. In accordance with our study, Ribas et al. (2018) indicated that there was a statistically significant positive association between anxiety symptoms and beta wave spectrum power.

In partial agreement with the present work, a study conducted by Knott et al. (1996) utilized the QEEG to compare patients with panic disorder with the control. The study revealed significant higher spectrum power of HF beta waves in right temporal and right parietal, and lower spectrum power of theta waves among cases than controls, which is similar to the current study results of HF beta waves. However, there were lower spectrum power values regarding delta and alpha and LF beta waves among cases compared with controls (Knott et al., 1996).

These differences between the two studies can be attributed to the difference in associated comorbidities and use of different QEEG program recording in 1996 when the study was done or even different environmental factors and societies.

The LF beta wave power showed no statistically significant difference between cases and controls, but the anxiety group showed a significantly higher amplitude in the right temporal and right parietal region.

This is different from Jalali et al. (2018) who reported a significantly higher absolute power of LF beta waves in the central region and LF beta wave in the occipital area among cases compared with controls.

Regarding alpha wave, there was no statistically significant difference between cases and controls according to amplitude and spectrum power.

On the contrary, a study conducted by Runyon et al. showed a higher right frontal alpha activity in cases with anxiety than controls (Runyon et al., 2018).

As regards the delta wave spectrum power, there was no statistically significant difference between cases and controls. This matches a study by Kim et al. (2021) to measure the wave’s absolute power changes in anxiety in adults. They concluded that there was no significant correlation between delta wave absolute power and anxiety scores. On the other hand, they found no statistically significant difference in the alpha, theta, and beta wave power, which is different from the current study results (Kim et al., 2021). This difference might be explained by the difference in the age group.

Findings of the current study support the validity of QEEG in discriminating between children with and without anxiety disorders.


  Conclusion Top


Children and adolescents with anxiety disorders have QEEG changes that coincide with their symptomatology proving that QEEG is a useful method in the assessment and diagnosis of anxiety disorders.

Limitation of the study

There are not enough studies to validate the QEEG role in the diagnosis of anxiety in children.

Further studies and larger sample size are required to evaluate the role of QEEG in the assessment of anxiety disorders in children and adolescents.

Acknowledgements

The authors acknowledge all the children, adolescents, and their guardians for participation in this study.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest to declare.





 
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    Figures

  [Figure 1], [Figure 2], [Figure 3], [Figure 4]
 
 
    Tables

  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6]



 

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