Depression Analysis and Diagnosis using Machine Learning

Abstract

The century's most famous event is the COVID-19 pandemic. Many people experience stress throughout the pandemic. Physical and mental health issues can be brought on by prolonged stress. It takes time, care, and experience to manually mark depression. The present method detects and diagnoses depression using EEG and ECG measurements. The goal of this work is to create a machine learning-based framework that uses EEG and ECG signals to assess and identify depression. System design calculations and tactics include extraction and selection methods for classification, including hybrid methods. The EEG and ECG functions are then submitted for categorization following retrieval. The ST segment, P wave, QRS wave, and ECG data are extracted as functions. The most significant characteristics examined from EEG signals were alpha band power, entropy, standard deviation, and Hjorth activity (HA). ECG data were analyzed using the Long Short-Term Memory (LSTM) Autoencoders and the RNN deep learning model methodology, while EEG signals were classified using the Support Vector Machine (SVM) and Convolutional Neural Network (CNN) techniques. Higher accuracy, sensitivity, selectivity, and specificity are achieved when using an RNN and an LSTM Autoencoders with two-dimensional sequence input as classifiers. For ECG signals, the current system achieves 93% accuracy. CNN outperforms SVM in terms of accuracy (97.69%) for EEG signals.