Stress Detection Using CNN on the WESAD Dataset

Stress, a pervasive psychological state, profoundly impacts individuals' well-being and may lead to various health issues. In this paper, the WESAD (Wearable Stress and Affect Detection) dataset serves as the primary data source, from which HRV (Heart Rate Variability) data extracted using PPG (Photoplethysmography) sensor is utilized. This data can be obtained in a non-invasive method and is instrumental in this analysis. The dataset comprises HRV data from 15 individual test subjects obtained from PPG sensors, providing a crucial foundation for the detection and classification of individuals into "stressed" or "not stressed" categories, thus offering a valuable tool for stress monitoring. This paper is a focused exploration into the CNN (Convolutional Neural Network) Deep Learning model, in which the feature selection process is carried out using Random Forest resulting in the selection of HR (Heart Rate), LF_BOXCOX (Low-Frequency Component - Box-Cox Transformed), MEDIAN_RR (Median of RR Intervals), SDI_BOXCOX (Standard Deviation 1 - Box-Cox Transformed), and SDRR_REL_RR (Standard Deviation of Relative RR Intervals) to optimize the model's performance. The model aims at advancing and refining stress detection methodologies to enhance overall well-being and achieves an accuracy of 98%.