Modelling of time-varying HRV using locally stationary processes

Estimates of heart rate variability (HRV), and particularly parameters related to high frequency HRV (HF-HRV), are in-creasingly used as a proxy of cardiac parasympathetic nervous system regulation. Reduced HF-HRV is related to attention deficits, depression, various anxiety disorders, long-term work related stress or burnout, and cardiovascular diseases [1,2]. In this work, a stochastic model, known as

Locally Stationary Processes, [3], is applied to HRV data sequences from 47 test participants. The model parameters are estimated with a novel inference method and regression using a number of available covariates is used to investigate their correlation with the stochastic model parameters.