Abstract
Heart Rate Variability (HRV) features are recognized as powerful indicators of various diseases, including heart failure, diabetes, and mental health disorders. Besides, HRV features are robust against noise, making them ideal for wearable devices. Despite their potential, HRV feature sets are limited by sample quantity. Direct augmentation often distorts underlying signal properties and interpretability. This study addresses this limitation by introducing spatial-domain, temporal-domain, and serial signal transformations for HRV feature-based augmentations. We further analyze the transformed signal using the Poincare plots to understand the effect of the transformation effect from the clinical perspective. Lastly, we design a stress detection deep learning model using the TILES-2018 database to verify the effectiveness of the augmented HRV features, which shows performance enhancements ranging from 1.05 % to 4.52%. Among the three domains of transformations, serial transformations yield the best results.