Machine Learning and Electroencephalogram Signal based Diagnosis of Depression

Depression is a psychological condition which hampers day to day activity (Thinking, Feeling or Action). The early detection of this illness will help to save many lives because it is now recognized as a global problem which could even lead to suicide. Electroencephalogram (EEG) signals can be used to diagnose depression using machine learning techniques. The dataset studied is public dataset which consists of 30 healthy people and 34 depression patients. The methods used for detection of depression are Decision Tree, Random Forest, Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional Long-Short Term Memory (Bi-LSTM), Gradient Boosting, Extreme Gradient Boosting (XGBoost) along with band power. Among Deep Learning techniques, CNN model got the highest accuracy with 98.13%, specificity of 99%, and sensitivity of 97% using band power features.

Automated summary

Depression is a global psychological condition that hampers day to day activity and can even lead to suicide. Machine learning techniques using EEG signals can be used to diagnose depression. Among these techniques, the CNN model achieved the highest accuracy of 98.13%, specificity of 99%, and sensitivity of 97% using band power features in the dataset of 30 healthy individuals and 34 depression patients.