In this paper a bivariate, time-variant model able to continuously measure the mutual interactions between heart rate and systolic blood pressure variability signals is presented. A recursive identification of the model parameters makes it possible to estimate, on a beat-to-beat basis, spectral low-frequency (LF) and high-frequency (HF) power, (LF/HF ratio) and cross-spectral (coherence and phase relationships between spectral peaks) indexes during nonstationary events. These indexes can be helpful in: 1) physiological study of autonomic nervous system mechanisms of cardiovascular control and 2) quantification and clinical evaluation of the neural and mechanical links between the two signals. In addition, an estimate of baroreceptive activation (alpha-gain) is continuously extracted. Before applying the model to cardiovascular signals, the reliability of the estimated parameters was tested on simulated signals. Subsequently, the model was applied to investigating vasovagal syncope episodes, aiming at the assessment of autonomic nervous system status and autonomic role in the dynamic phenomena which lead to syncope. The proposed model, which provides noninvasive beat-to-beat evaluation of the autonomic events, may be useful in the description of the syncopal episodes and in the comprehension of the complex physiological mechanisms of syncope.

Multivariate time-variant identification of cardiovascular variability signals: a beat-to-beat spectral parameter estimation in vasovagal syncope / L.T. Mainardi, A.M. Bianchi, R. Furlan, S. Piazza, R. Barbieri, V. Di Virgilio, A. Malliani, S. Cerutti. - In: IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING. - ISSN 0018-9294. - 44:10(1997 Oct), pp. 978-989.

Multivariate time-variant identification of cardiovascular variability signals: a beat-to-beat spectral parameter estimation in vasovagal syncope

R. Furlan;
1997

Abstract

In this paper a bivariate, time-variant model able to continuously measure the mutual interactions between heart rate and systolic blood pressure variability signals is presented. A recursive identification of the model parameters makes it possible to estimate, on a beat-to-beat basis, spectral low-frequency (LF) and high-frequency (HF) power, (LF/HF ratio) and cross-spectral (coherence and phase relationships between spectral peaks) indexes during nonstationary events. These indexes can be helpful in: 1) physiological study of autonomic nervous system mechanisms of cardiovascular control and 2) quantification and clinical evaluation of the neural and mechanical links between the two signals. In addition, an estimate of baroreceptive activation (alpha-gain) is continuously extracted. Before applying the model to cardiovascular signals, the reliability of the estimated parameters was tested on simulated signals. Subsequently, the model was applied to investigating vasovagal syncope episodes, aiming at the assessment of autonomic nervous system status and autonomic role in the dynamic phenomena which lead to syncope. The proposed model, which provides noninvasive beat-to-beat evaluation of the autonomic events, may be useful in the description of the syncopal episodes and in the comprehension of the complex physiological mechanisms of syncope.
autoregressive spectral estimation ; blood pressure variability ; heart rate variability ; multivariate modeling ; recursive identification ; vasovagal syncope
Settore ING-INF/06 - Bioingegneria Elettronica e Informatica
Settore MED/09 - Medicina Interna
ott-1997
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/192836
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