Stress Monitoring With Heart Rate Variability Using Deep Learning

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.