Quantitative EEG Features to Diagnose Anxiety Disorders

According to World Health Organization (WHO) report in ۲۰۱۹, ۱ of ۸ people live with amental disorder. Anxiety disorders are the most common type of mental disorders. Anxiety disordersinclude disorders that share features of excessive fear and anxiety and related behavioral disturbances.Diagnosis of anxiety disorder is the first and the most important step towards its treatment.While the clinical method is yet the most reliable method of diagnosis, use of biological signals, suchas Electrocardiogram, electroencephalogram, etc. has attracted researchers’ attention.In this paper, we study the EEG signals and the information they convey to find a new way to diagnoseanxiety disorders. For this purpose, we have selected different EEG features and perform ۵ differentclassification methods on them. It is shown that EEG features can be a bio-marker of anxiety disorders.Obtained results show that functional connectivity (FC) feature sets achieved better results withRandom Forest.

کلیدواژه ها:

Anxiety disorder ، Electroencephalogram ، EEG ، Machine learning