A method is introduced for the analysis of heart rate and arterial blood pressure variability signals by means of parametric spectral estimation using autoregressive (AR) modeling and a classical bivariate Fourier approach. Two bands are particularly observed in autospectra with high coherence (one at 0. 1 eq. Hz and the other at the respiratory frequency), suggesting that there is a significant and systematic phase shift between the two series which are synchronous to the cardiac cycle. The same mechanism of variability may be supposed acting on the two signals, and different hypotheses about their origin may be introduced for the two distinct rhythms. Results for normal and pathological subjects show that the analysis of heart rate variability can characterize important pathologies such as diabetes, hypertension and myocardial infarction. The approach is extremely sensitive and suitable for both physiological studies and clinical purposes.
|Title of host publication||Computers in Cardiology|
|Editors||Kenneth L. Ripley|
|Number of pages||4|
|Publication status||Published - 1985|
ASJC Scopus subject areas
- Cardiology and Cardiovascular Medicine