RESUMEN
Objective: Anxiety disorder (AD) is a common disabling disease. The prolonged disease course may lead to impaired cognitive performance, brain function, and a bad prognosis. Few studies have examined the effect of disease course on brain function by electroencephalogram (EEG). Methods: Resting-state EEG analysis was performed in 34 AD patients. The 34 patients with AD were divided into two groups according to the duration of their illness: anxious state (AS) and generalized anxiety disorder (GAD). Then, EEG features, including univariate power spectral density (PSD), fuzzy entropy (FE), and multivariable functional connectivity (FC), were extracted and compared between AS and GAD. These features were evaluated by three previously validated machine learning methods to test the accuracy of classification in AS and GAD. Results: Significant decreased PSD and FE in GAD were detected compared with AS, especially in the Alpha 2 band. In addition, FC analysis indicated that GAD patients' connection between the left and right hemispheres decreased. Based on machine learning, AS and GAD are classified on a six-month criterion with the highest classification accuracy of up to 0.99 ± 0.0015. Conclusion: The brain function of patients is more severely impaired in AD patients with longer illness duration. Resting-state EEG demonstrated to be a promising examination in the classification in GAD and AS using machine learning methods with better classification accuracy.
RESUMEN
Narcolepsy is characterized by uncontrollable excessive daytime sleepiness, paroxysmal cataplexy, sleep paralysis, and hallucinations. It is often misdiagnosed as psychiatric disorders such as depression and schizophrenia, resulting from the overlap in symptoms and a lack of understanding of narcolepsy. In the present study, three cases of narcolepsy misdiagnosed as depression, dissociative disorder, and schizophrenia are presented to emphasize the high occurrence of the misdiagnosis of narcolepsy in clinical practice. The main reasons for this dilemma are attributed to the lack of adequate sleep, medicine, education, as well as specialized professional technicians. A multi-disciplinary team composed of psychiatrists and sleep specialists should be established to deal with this problem.