Complexity Analysis of EEG in Patients With Social Anxiety Disorder Using Fuzzy Entropy and Machine Learning Techniques

Abstract

The diagnosis of social anxiety disorder (SAD) is of great consequence not only due to its impacts on the individual and society but also the expenditures to the national health systems. There is yet a deficiency of objective neurophysiological information to assist the present clinical SAD diagnosis. The main objective of this study is to analyze the electroencephalogram (EEG) complexity of 88 SAD subjects, subdivided into 4 balanced groups (22 severe, 22 moderate, 22 mild, and 22 healthy controls (HCs) using Fuzzy Entropy measure (FE) and machine learning algorithms. In addition, this study aimed at designing a computer-aided diagnosis system to identify the severity of SAD (severe, moderate, mild, and HC) in different EEG frequency bands (delta, theta, alpha, and beta). The experimental results showed that among the HC and the three considered levels of SAD, SAD patients in fast-waves exhibited significantly less FE values in resting-state compared with HCs (p ≤ 0.05). The EEG complexity analysis showed a discriminatory neuronal activity over the frontoparietal and occipital regions between SAD patients and HCs. Additionally, the FE values measured in the resting-state were positively correlated with Social Interaction Anxiety Scale (SIAS) scores in fast-waves (beta and alpha), indicating that the regional FE measures are putative biomarkers in assessing the clinical symptoms of SAD. Also, the classification results demonstrated that the proposed method outperformed the state of the art methods with an accuracy of 86.93 %, sensitivity of 92.46%, and specificity of 95.32% with the Naive Bayes (NB) classifier. This study emphasizes the viability of quantitative FE measures and the specific combinations involving the chosen classifiers could be considered as an alternative biomarker for future clinical SAD recognition.

Machine learning for the detection of social anxiety disorder using effective connectivity and graph theory measures

“…Features from these frequency bands were chosen to provide optimum accuracy with optimal order. The appropriate order (p) of the model was calculated by the Akaike Information Criterion (AIC) as described in our previous studies ( 20 , 26 , 45 ). In this study, the optimum order is 5.…”

“…In our previous study, we applied machine learning techniques to study the EEG complexity ( 26 ) and deep learning models with PDC features ( 27 ) to classify four different subtypes of SAD. Our study found that the deep learning model outperformed the machine learning models, achieving a classification accuracy of 92.86% using a combination of CNN and LSTM, and the most important features for classification were located in the default mode network (DMN) of the brain.…”

Difference analysis of multidimensional electroencephalogram characteristics between young and old patients with generalized anxiety disorder

“…(1) Compared with the Young_GAD patients, the Old_GAD patients had higher AP and FE values in beta rhythm in most brain regions, and there were significant statistical differences in the forehead, central, and parietal regions. The increased AP value of beta indicated that the brain activity is in a state of tension and neural nervousness, and the increased FE value indicated increased complexity ( Al-Ezzi et al, 2022 ). Shen et al (2022) has revealed that GAD patients have higher PSD and FE than healthy adults in beta rhythm.…”

“…The Food and Drug Administration 4 (FDA4) approved the value of theta/beta as a biomarker for attention deficit and hyperactivity disorder (ADHD) ( Newson and Thiagarajan, 2019 ). Al-Ezzi et al (2022) showed that the FE value of social anxiety disorder (SAD) in beta rhythm was positively correlated with the Social Interaction Anxiety Scale (SIAS). On the other hand, more evidence also points to a high correlation between EEG characteristics of beta rhythm and age change.…”

Serotonin-related Mechanisms in the Etiology and Pharmacotherapy of Social Phobia, A Review: Serotonin and Social Phobia

“…Nevertheless, functional solutions have been proposed, including the definition of biomarkers that can distinguish between these diseases, which may contribute to both correct diagnosis and selection of appropriate therapy, and also it could play a role in tracking patient status [12,13]. The promising biomarkers are described in different psychiatric disorders and include alterations in the genetic, epigenetic, structure, function of the brain, endocrine system, immune system, and neuropsychological aspects [13,14,15,16].It is widely accepted that social phobia is a polygenic and complex condition, however, few altered genetic and epigenetic factors are identified as causative agents.…”