HRV Based Stress Detection Using Convolutional Neural Networks (CNNs)

Stress detection is crucial for maintaining individual well-being and preventing potential health complications. In this paper, we explore the use of Convolutional Neural Networks (CNNs) for stress detection based on Heart Rate Variability (HRV) signals. The study leverages the Multi-Modal Dataset (MMSD), a comprehensive repository encompassing HRV data from 74 subjects across distinct phases: relaxation, stress induction, and recovery. We explore the effectiveness of CNNs compared to traditional machine learning algorithms in discerning stress states using HRV data. Results reveal promising performance of CNNs in accurately identifying stress, showcasing their potential for real-time stress monitoring applications. This research contributes to advancing the field of stress detection, offering insights into the application of deep learning techniques on physiological data for enhanced stress detection strategies.