Non invasive EEG signal processing framework for real time depression analysis

TLDR: This work shows that linear analysis of EEG can be an efficient method for identifying depressed patients from normal subjects and it is recommended that this analysis may be a supporting aid for psychiatrists to identify severity level of depressed patients.

Abstract: There is a long list of words that describe depression; sadness, unhappiness, sorrow, dejection, low spirit, despondency, woe, gloom, pessimism, desolation, despair, hopelessness, moodiness, and a host of others. This study throws light upon the contribution EEG signal for depression analysis. In this paper, classification of depressed patients from normal subjects are identified by using EEG signal. Experimental results are carried out with the help of 13 depressed patients and 12 normal subjects. This paper tries to classify person's mental state either normal or depressed with the help of EEG signal using signal processing technique FFT and machine learning technique SVM. These noninvasive signal techniques are useful for detection of depression disorders through EEG signals. The proposed work is compared with the other methods. The diagnosis is done and appropriate remedies are taken according to scale of the depression in the patient. This work shows that linear analysis of EEG can be an efficient method for identifying depressed patients from normal subjects. It is recommended that this analysis may be a supporting aid for psychiatrists to identify severity level of depressed patients.