Summary
Objective:
We introduce an algorithm for the automatic decomposition of Wigner Distribution
(WD) and we applied it for the quantitative extraction of Heart Rate Variability (HRV)
spectral parameters during non-stationary events. Early response to tilt was investigated.
Methods:
Quantitative analysis of multi-components non-stationary signals is obtained through
an automatic decomposition of WD based on least square (LS) fitting of the instantaneous
autocorrelation function (ACF). Through this approach the different signal and interference
terms which contributes to the ACF may be separated and their parameters (instantaneous
frequency and amplitude) quantified. A beat-to-beat monitoring of HRV spectral components
is obtained.
Results:
Analysis of simulated signals demonstrated the capability of the proposed approach
to track and separate the signal components. Analysis of HRV data evidenced different
dynamics in the early Autonomic Nervous System (ANS) response to tilt.
Conclusions:
The novel approach to the quantification of the beat-to-beat HRV spectral parameters
obtained from decomposition of Wigner distribution was demonstrated to be effective
in the analysis of HRV data. Relevant physiological information about the dynamics
of the early sympathetic response to tilt were obtained. The method is a general approach
which may be employed for a quantitative time-frequency analysis of non-stationary
biological signals.
Keywords
Time-frequency distribution - autonomic nervous system - least square fitting
