Modelling of time-varying HRV using locally stationary processes

Rachele Anderson, Peter Jönsson, Maria Sandsten

Research output: Contribution to conferenceAbstract


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.

Original languageEnglish
Publication statusPublished - 2017
EventEMBEC'17 & NBC'17 Tampere, Finland -
Duration: 1980-Jan-01 → …


ConferenceEMBEC'17 & NBC'17 Tampere, Finland
Period80-01-01 → …

Swedish Standard Keywords

  • Psychology (501)


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