S. Cerutti

Bio: S. Cerutti is an academic researcher from University of Brescia. The author has contributed to research in topics: Autoregressive model & Blood pressure. The author has an hindex of 3, co-authored 7 publications receiving 199 citations.

Spectral decomposition in multichannel recordings based on multivariate parametric identification

Abstract: A method of spectral decomposition in multichannel recordings is proposed, which represents the results of multivariate (MV) parametric identification in terms of classification and quantification of different oscillating mechanisms. For this purpose, a class of MV dynamic adjustment (MDA) models in which a MV autoregressive (MAR) network of causal interactions is fed by uncorrelated autoregressive (AR) processes is defined. Poles relevant to the MAR network closed-loop interactions (cl-poles) and poles relevant to each AR input are disentangled and accordingly classified. The autospectrum of each channel can be divided into partial spectra each relevant to an input. Each partial spectrum is affected by the cl-poles and by the poles of the corresponding input; consequently, it is decomposed into the relevant components by means of the residual method. Therefore, different oscillating mechanisms, even at similar frequencies, are classified by different poles and quantified by the corresponding components. The structure of MDA models is quite flexible and can be adapted to various sets of available signals and a priori hypotheses about the existing interactions; a graphical layout is proposed that emphasizes the oscillation sources and the corresponding closed-loop interactions. Application examples relevant to cardiovascular variability are briefly illustrated.

Non-linear dynamics in the beat-to-beat variability of sympathetic activity in decerebrate cats.

Abstract: Non-linear interactions between low-frequency rhythms (0.1 Hz) of beat-to-beat variability series of sympathetic discharge and respiratory rhythm (0.3 Hz) are observed in decerebrate artificially ventilated cats. Simple graphical tools as Poincare and recurrence maps are used to detect, in a qualitative way, phase-locking phenomena. Non-parametric bispectral analysis is also carried out to quantify the degree of second-order coupling between oscillations at different frequencies.

Beat-to-beat variability of microvascular peripheral resistances assessed with a non-invasive approach

Abstract: The pressure-flow relationship at the peripheral level is non-invasively studied in human subjects: the impedance function and the beat-to-beat variability series of microvascular peripheral resistance are estimated. The frequency content of this variability signal is compared to those of more classical variability series at rest and during mild supine physical exercise.

Multivariate ARMA spectral decomposition in the assessment of cardiovascular variabilities

Abstract: Multivariate spectral analysis is able to describe the interactions between heart rate and arterial pressure variabilities; therefore, it provides a spectral decomposition based on which signal is driven more directly or on which closed-loop resonance is involved. So, it provides further insight in the genesis of rhythms, beyond the classical definition of low frequency (LF) and high frequency (HF) components related to mono-variate spectral analysis. The method of spectral decomposition is presented both for the identification of bi-variate autoregressive models, which is a general signal processing tool, and for a dynamic adjustment model specific for cardiovascular variabilities. Preliminary results on conscious dogs under various sympathetic stimuli enhancing LF rhythms confirm the existence of different mechanisms which contribute to these waves. >

HRV during exercise in sedentary subjects and athletes

Abstract: The power-spectral analysis of heart-rate variability (HRV) is used to compare situations of different muscular exercise in nine sedentary males (n=9) and eight professional cyclists. The changes of low-frequency (LF) and high-frequency (HF) spectral peaks, together with the weight of the very low frequencies (VLF), are described. Their relationship to changes in autonomic activation is discussed. The importance of the LF/HF ratio as a marker of sympathovagal balance is confirmed at rest and at low exercise level. The observed reduction of the LF rhythm power and the increase of the VLF power at the higher exercise level suggest the necessity of further investigation. >

Recurrence Quantification Analysis

Abstract: The complexity, nonlinearity, and nonstationarity of biological time series typically defy quantitative description. Living systems are governed by numerous, continuously changing, interacting variables in the presence of noise. Such conditions often challenge traditional methods such as Fourier transforms. An approach for assessing such nondeterministic complexity is recurrence quantification analysis (RQA). Strategies implementing quantification of recurrences have often been successful in diagnosing changes in nonstationary signals not easily detected by traditional methods. Keywords: recurrence; time series analysis; signal analysis

Coupling functions:universal insights into dynamical interaction mechanisms

Abstract: The dynamical systems found in Nature are rarely isolated. Instead they interact and influence each other. The coupling functions that connect them contain detailed information about the functional mechanisms underlying the interactions and prescribe the physical rule specifying how an interaction occurs. Here, we aim to present a coherent and comprehensive review encompassing the rapid progress made recently in the analysis, understanding and applications of coupling functions. The basic concepts and characteristics of coupling functions are presented through demonstrative examples of different domains, revealing the mechanisms and emphasizing their multivariate nature. The theory of coupling functions is discussed through gradually increasing complexity from strong and weak interactions to globally-coupled systems and networks. A variety of methods that have been developed for the detection and reconstruction of coupling functions from measured data is described. These methods are based on different statistical techniques for dynamical inference. Stemming from physics, such methods are being applied in diverse areas of science and technology, including chemistry, biology, physiology, neuroscience, social sciences, mechanics and secure communications. This breadth of application illustrates the universality of coupling functions for studying the interaction mechanisms of coupled dynamical systems.

Assessing baroreflex gain from spontaneous variability in conscious dogs: role of causality and respiration.

Abstract: A double exogenous autoregressive (XXAR) causal parametric model was used to estimate the baroreflex gain (αXXAR) from spontaneous R-R interval and systolic arterial pressure (SAP) variabilities in...

Movement-related frequency modulation of beta oscillatory activity in the human subthalamic nucleus

Abstract: Event-related changes of brain electrical rhythms are typically analysed as amplitude modulations of local field potential (LFP) oscillations, like radio amplitude modulation broadcasting. In telecommunications, frequency modulation (FM) is less susceptible to interference than amplitude modulation (AM) and is therefore preferred for high-fidelity transmissions. Here we hypothesized that LFP rhythms detected from deep brain stimulation (DBS) electrodes implanted in the subthalamic nucleus (STN) in patients with Parkinson's disease could represent movement-related activity not only in AM but also in FM. By combining adaptive autoregressive identification with spectral power decomposition, we were able to show that FM of low-beta (13-20 Hz) and high-beta (20-35 Hz) rhythms significantly contributes to the involvement of the human STN in movement preparation, execution and recovery, and that the FM patterns are regulated by the dopamine levels in the system. Movement-related FM of beta oscillatory activity in the human subthalamic nucleus therefore provides a novel informational domain for rhythm-based pathophysiological models of cortico-basal ganglia processing.

Quantifying the strength of the linear causal coupling in closed loop interacting cardiovascular variability signals.

Abstract: The coherence function measures the amount of correlation between two signals x and y as a function of the frequency, independently of their causal relationships. Therefore, the coherence function is not useful in deciding whether an open-loop relationship between x and y is set (x acts on y, but the reverse relationship is prevented) or x and y interact in a closed loop (x affects y, and vice versa). This study proposes a method based on a bivariate autoregressive model to derive the strength of the causal coupling on both arms of a closed loop. The method exploits the definition of causal coherence. After the closed-loop identification of the model coefficients, the causal coherence is calculated by switching off separately the feedback or the feedforward path, thus opening the closed loop and fixing causality. The method was tested in simulations and applied to evaluate the degree of the causal coupling between two variables known to interact in a closed loop mainly at a low frequency (LF, around 0.1 Hz) and at a high frequency (HF, at the respiratory rate): the heart period (RR interval) and systolic arterial pressure (SAP). In dogs at control, the RR interval and the SAP are highly correlated at HF. This coupling occurs in the causal direction from the RR interval to the SAP (the mechanical path), while the coupling on the reverse causal direction (the baroreflex path) is not significant, thus pointing out the importance of the direct effects of respiration on the RR interval. Total baroreceptive denervation, by opening the closed loop at the level of the influences of SAP on RR interval, does not change these results. In elderly healthy men at rest, the RR interval and SAP are highly correlated at the LF and the HF. At the HF, a significant coupling in both causal directions is found, even though closed-loop interactions are detected in few cases. At the LF, the link on the baroreflex pathway is negligible with respect to that on the reverse mechanical one. In heart transplant recipients, in which SAP variations do not cause RR interval changes as a result of the cardiac denervation, the method correctly detects a significant coupling only on the pathway from the RR interval to the SAP.