Depression Detection Using ECG: Machine Learning

Thomala Gowthami, Yennam Mary Poojitha, Satri Tabita, Yarrajodu Nandini, Palle Sujatha

2025

Abstract

The major mental disorders affecting the society. The research utilizes electrocardiogram (ECG) signals together with advanced Support Vector Machine, Random Forest, Convolutional Neural Network and Long Short Term Memory based machine learning models to objectively predict depression using heart rate variation, age, and other ECG derived features. With its promise of an early and precise detection of depressive patterns, this method can well revolutionize mental health diagnostics with a noninvasive and cost effective detection which can be brought into real time diagnosis and telemedicine. The major preprocessing steps like noise filtering and normalization to improve the data quality and to automatically extract the features as comparison to manual feature engineering are important which the key to this approach are. Additionally, the system could be enhanced by the integration of other physiological signals, e.g. the EEG and the skin conductance, which would allow the system to cope with ECG signal variability and to require high quality data sets for the system to find robust and reliable implementations in real world applications.

Paper Citation

in Harvard Style

Gowthami T., Poojitha Y., Tabita S., Nandini Y. and Sujatha P. (2025). Depression Detection Using ECG: Machine Learning. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 232-237. DOI: 10.5220/0013925700004919

in Bibtex Style

@conference{icrdicct`2525,
author={Thomala Gowthami and Yennam Poojitha and Satri Tabita and Yarrajodu Nandini and Palle Sujatha},
title={Depression Detection Using ECG: Machine Learning},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={232-237},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013925700004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25
TI - Depression Detection Using ECG: Machine Learning
SN - 978-989-758-777-1
AU - Gowthami T.
AU - Poojitha Y.
AU - Tabita S.
AU - Nandini Y.
AU - Sujatha P.
PY - 2025
SP - 232
EP - 237
DO - 10.5220/0013925700004919
PB - SciTePress