Study On EEG Power Spectrum Of Depression With Anxiety Symptoms

Objective:Depression is a common psychiatric disorder with a lifetime prevalence of approximately 20% in the general population and is associated with high rates of disability,impaired psychosocial functioning,and reduced life satisfaction.As we all know,there are many subtypes of depression,and the prognosis of patients with different subtypes is also different.Anxious symptoms are one of the common clinical features of depression.Anxious Depression with anxiety symptoms(AND)reflects the characteristics of poor drug tolerance,low compliance,and high suicide risk,and is closely related to the severity and prognosis of depression.Therefore,to explore the specific etiology and pathological mechanism of AND(one of the clinical subtypes of depression),to provide reference for distinguishing symptoms and guiding clinical drug treatment.EEG has been widely used in clinical practice as a detection tool for neuropsychiatric diseases due to its advantages of non-invasiveness and high temporal resolution.Resting state reflects endogenous brain tissue activity and is thought to convey valuable information about how different brain structures communicate.The resting state generally represents a state of no task,and many results in the resting state are relatively stable.Resting state is an ideal research direction of brain function,because it does not need to do corresponding tasks,and after strict control of various parameters,corresponding norm can be made to represent many indicators.Neural activity assessed by EEG was mainly estimated by applying time series analysis(Fast Fourier Transform)to the power spectrum of the signal in five main frequency bands(δ: 0-4 Hz,theta: 4-8Hz,α: 8-12 Hz,β: 12-30 Hz,γ: 30-100 Hz).Studies have found the mechanism of resting-state EEG spectral activity and depression,and the association between depression-related subtypes and EEG biomarkers has not been further confirmed.In this study,first-episode untreated depression patients were selected,resting-state EEG data were collected,and EEG power spectrum analysis was performed on the EEG characteristics of depression with anxiety symptoms,in order to find the characteristics of measuring biomarkers to identify or assist in the diagnosis of diseases,and provide a new strategy for the treatment of patients with depression with anxiety symptoms.Methods:This study included 76 patients with AND,57 patients with Nonanxious Depression without anxiety symptoms(NAD),and 59 patients with Anxiety Disorder(AN).Demographic and clinical data were compared using one-way ANOVA and t-test.The t-test was used to analyze the scale scores to improve the accuracy of the study,and SPSS26.0 software was used for statistical analysis.After preprocessing the EEG data with MATLAB software,frequency domain analysis was performed in each frequency band to analyze the EEG differences of the three groups of patients.Results:1.There were no significant differences in age,gender,years of education and marital status among the three groups.There was no significant difference in the HAMD total score between the AND and NAD groups among the three groups.There was a difference in the HAMD total score between the AND group and the AN group,and the AND group was higher.There was a difference in the HAMD total score between the NAD group and the AN group,and the NAD group was higher.Among the three groups,there was a difference in the total HAMA score between the AND group and the NAD group,and the AND group was higher.There was a difference in the total HAMA score between the AND group and the AN group,and the AN group was higher.There was a difference in the HAMA total score between the NAD group and the AN group,and the AN group was higher.2.The EEG power of the three groups of patients was different in δ band,θ band,αband and γ band.The EEG power of the AND group and the NAD group was different inθ band and γ band;the EEG power of the AND group and the AN group was different inδ band,θ band,α band and γ band;the EEG power of the NAD group and the AN group was different in α band.Conclusion:1.There were differences in EEG power spectrum between AND and NAD in θband and γ band;2.There were differences in EEG power spectrum between AND and AN patients in δ band,θ band,α band and γ band.