Depression Analysis Based on EEG and ECG Signals

Abstract

In covid -19 situation, most people suffered from stress. Continuous stress can lead to severe psychological and even physical disorders. To detect the depression manually is time-consuming, tedious, and requires expertise. The present system is used for detecting and analyzing depression based on EEG and ECG signals. The system layout strategies and calculations include extraction and choice strategies for classification and for deteriorating techniques, as well as combination methodologies. The EEG and ECG feature are extracted and sent for the classification. From ECG signals the ST segment, P wave and QRS wave as features extracted. The most prominent features are analyzed from EEG signals are Hjorth activity (HA), standard deviation, entropy and band power alpha. The Long Short-Term Memory (LSTM) autoencoder and RNN deep learning model approach were used for depression analysis.