Vol. 3 No. 2 (2025): Issue - 02
Articles

Stress Monitoring With Heart Rate Variability Using Deep Learning

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  • B K Preethika Raj,
  • D Nithish,
  • B Manjunatha,
  • G Praveen,
  • G Sandeep,
  • P Chandra Sekhar
B K Preethika Raj
Department of Computer Science Engineering, Annamacharya Institute of Technology and Sciences (Autonomous) Kadapa, Andra Pradesh, India
D Nithish
Department of Computer Science Engineering, Annamacharya Institute of Technology and Sciences (Autonomous) Kadapa, Andra Pradesh, India
B Manjunatha
Department of Computer Science Engineering, Annamacharya Institute of Technology and Sciences (Autonomous) Kadapa, Andra Pradesh, India
G Praveen
Department of Computer Science Engineering, Annamacharya Institute of Technology and Sciences (Autonomous) Kadapa, Andra Pradesh, India
G Sandeep
Department of Computer Science Engineering, Annamacharya Institute of Technology and Sciences (Autonomous) Kadapa, Andra Pradesh, India
P Chandra Sekhar
Department of Computer Science Engineering, Annamacharya Institute of Technology and Sciences (Autonomous) Kadapa, Andra Pradesh, India
DOI: https://doi.org/10.5281/zenodo.15266886

Published 2025-04-23

Keywords

  • Stress detection,
  • heart rate variability,
  • convolutional neural network,
  • feature extraction

How to Cite

B K Preethika Raj, D Nithish, B Manjunatha, G Praveen, G Sandeep, & P Chandra Sekhar. (2025). Stress Monitoring With Heart Rate Variability Using Deep Learning. Milestone Transactions on Medical Technometrics, 3(2), 194–200. https://doi.org/10.5281/zenodo.15266886

Abstract

Prolonged stress can lead to mental health issues like anxiety and sleep disorders. Heart Rate Variability (HRV) serves as a key physiological marker for stress detection. Unlike heart rate, HRV measures the variation in time intervals between heartbeats (RR intervals). This study proposes a CNN-based model for classifying stress into no stress, interruption stress, and time pressure stress using HRV features. Evaluated on the SWELL-KW dataset, the model achieves 99.9% accuracy, outperforming existing methods. Feature extraction techniques, such as ANOVA, further validate the significance of HRV features in stress detection.

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References

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