Poincaré Analysis based on Short-term Heart Rate Variability Data for Stress Evaluation

Dynamic changes in autonomic stress responses may provide details on autonomic nervous system functions. Time-varying evaluation can be achieved with a sliding window, however, in order to learn dynamic changes, an evaluation method needs to not only conduct calculation with a short sliding step but also derive evaluation indices with a narrow window. Stress analysis using HRV data shorter than one minute is still a challenge in this field. This paper investigates a Poincar plot analysis method for stress evaluation based on short term heart rate variability (HRV) data. First a sliding window, with no overlap, is used to segment data in order to form Poincaré plots. Then a simple index, which corresponds to mean distance between two adjacent points in the plot, is calculated on each evaluation window. The window length is defined with time duration and four lengths are examined in this paper, namely, 15, 30, 45, and 60 s. Two mental stress induction experiments, mental arithmetic and Stroop color-word tests, are utilized to validate the proposed method.