Stress Net: Multimodal Stress Detection using ECG and EEG Signals

Abstract

This research work introduces Integrity of Time Domain Features & Machine Learning for Stress Classification using ECG & EEG Signals. Stress is a prevalent mental health issue in our daily lives, affecting many individuals. The impact of stress can lead to various problems, including heart attacks and depression. This research work aims to identify anxiety through a physical examination using both EEG and ECG signals. By analyzing and monitoring these signals, we can improve stress detection exactness, ultimately identifying and addressing mental health problems. This research work is used to prevent early detection of diseases such as depression and suicidal attempts. This task can benefit society as a whole. Moreover, using ECG signals to assess
cardiovascular and related risk factors in the early stages has been explored through machine learning techniques.