Ultra-short-term HRV analysis in a dynamic scenario using the traditional and functional approach

Heart rate variability (HRV) analysis is a commonly used method, not only for assessing cardiac health but also for detecting stress and emotional arousal. However, analyzing heart rate recordings from very short (less than 1 minute) dynamic scenarios presents challenges due to their short duration, measurement artifacts, and the presence of trends. This article presents two approaches for analyzing such heart rate data. Firstly, the most common HRV metrics are reviewed, and their potential suitability is evaluated through literature review and simulated data analysis. Secondly, a novel functional approach is introduced. Both approaches are assessed and compared using real-world dataset from a dynamic simulated attack scenario. Among the selected traditional HRV metrics, RMSSD and SD1 were found to be the most suitable for analyzing ultra-short-term dynamic scenarios. The functional approach yielded similar conclusions to the traditional approach while providing additional possibilities for further analysis and deeper interpretation of results. Finally, the advantages, disadvantages, and potential applications of both approaches are discussed.