Features Extraction and Depression Level Prediction by Using EEG Signals

Depression is a mood disorder that causes a persistent feeling of sadness and loss of interest. It affects how you feel, think and behave and can lead to a variety of emotional and physical problems. Depressed people may feel sad, anxious, empty, worthless, guilty, irritable, or restless. They may lose interest in activities that once were pleasurable, experience loss of appetite or overreacting, or problems concentrating, remembering details or making decisions. According to the WHO in India 36% (data of 2016) of population suffering from depression. Day by day it is becoming global crisis. It is estimated that by the year 2020 if current trends for demographic and epidemiological transition continue, the burden of depression will increase to 5.7% of the total burden of disease and it would be the second leading cause of disability-adjusted life years. Severe cases of depression interfere with the common live of patients. For those patients a strict monitoring is necessary in order to control the progress of the disease and to prevent undesirable side effects. Diagnosing depression in the early curable stage is very important. Electroencephalogram (EEG) signals are obtained for publicly available database are processed in MATLAB. This can be useful in classifying subjects with the disorders using classifier tool in it. Primarily the EEG signals were read using EDF browser software and the signals were fed into MATLAB to get log power spectral density from EEG bands. The features are extracted from different frequency bands.