Stress Net: Multimodal Stress Detection using ECG and EEG Signals

Lakshmi, K. and Chitra, K. (2024) Stress Net: Multimodal Stress Detection using ECG and EEG Signals. Journal of Data Science, 2024 (59). pp. 1-8. ISSN 2805-5160

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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.

Item Type:	Article
Uncontrolled Keywords:	EEG, ECG, Stress Net, Multimodal, AUC, Kappa
Subjects:	Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer)
Depositing User:	Unnamed user with email masilah.mansor@newinti.edu.my
Date Deposited:	28 Nov 2024 04:34
Last Modified:	28 Nov 2024 04:34
URI:	http://eprints.intimal.edu.my/id/eprint/2066

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