Time-variant spectral estimation of heart rate variability signal

The authors describe some time-variant algorithms of autoregressive (AR) identification that result in obtaining a new set of AR parameters for each data sample. The performances of the algorithms were tested on simulated series to better understand their capability in tracking abrupt or constant-rate changes in the signal. A power spectrum density was obtained at each sample and a compressed spectrum array (CSA) graph is plotted. The algorithms were then applied in the study of the heart rate variability signal in dogs during coronary occlusion and in human subjects during transient ischemic episodes. Spectral parameters were obtained on a beat-to-beat basis, for a better comprehension of the dynamic role of the autonomic nervous system in this pathology.