Stress Monitoring With Heart Rate Variability Using Deep Learning
Year: 2025
Type: article
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.
Authors: B K Preethika Raj, et al.
Language: English
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W6949430975
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