Time-variant estimation of the spectral parameters of Heart Rate Variability

Recursive Auto-Regressive (AR) identification algorithms, applied to the study of Heart rate Variability (HRV), allow to investigate transient biological phenomena. Each time the AR parameters are updated, the positions of poles must be evaluated in order to quantify frequency and power of spectral peaks by means of a spectral decomposition based on the residual integration method. We describe a recursive method for pole tracking that estimate the new pole positions on the basis of the AR parameters variations, in a more efficient way than traditional factorization algorithms. The method is suitable for an on-line monitoring of traditional HRV parameters, which measure the symphato-vagal balance elicited by the control mechanisms of cardiovascular system.