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Giuseppe Baselli

Publications and source records attributed to Giuseppe Baselli.

4 recordsLinked to original sources

Coupling arterial windkessel with peripheral vasomotion: modeling the effects on low-frequency oscillations.

Arterial pressure (AP) and heart rate (HR) waves have long been recognized as an important sign of cardiovascular regulation, however, the underlying interactions involving vasomotion, arterial mechanisms and neural regulation have not been clarified. With the aid of simple dynamical models consisting of active peripheral vascular districts (PVDs) fed by a compliant/resistant arterial tree, the relationship between local AP and flow and systemic AP waves were analyzed. A PVD was described as a nonlinear flow regulation loop. Various feedback dynamics were experimented and general properties were focused. The PVDs displayed a region of active flow compensation against pressure changes, in which self-sustained low-frequency (LF, 0.1 Hz) appeared. Oscillations critically depended on parameter, Teq, analogous to a windkessel time constant, proportional to arterial compliances: a value of about 2 s (consistent with a normal pulse pressure) performed a buffering effect essential for LF oscillations in peripheral flow; conversely, stiffer arteries damped LF vasomotion. Two PVDs fed by a common compliance oscillated in phase opposition; the consequent negative interference cancelled systemic AP waves, even in presence of large peripheral oscillations. The partial disruption of phase opposition by a common neural drive oscillating at a LF proximal to that of the PVDs unveiled LF waves in AP. Also, several PVDs with randomly different natural frequencies displayed a tendency to reciprocal cancellation, while a limited neurally induced phase alignment unmasked LF oscillations at systemic level. It is concluded that vasomotion, arterial compliances and, neural drives are all elements which may cooperate in forming AP waves.

Animals↗

Adaptive autoregressive identification with spectral power decomposition for studying movement-related activity in scalp EEG signals and basal ganglia local field potentials.

We propose a method that combines adaptive autoregressive (AAR) identification and spectral power decomposition for the study of movement-related spectral changes in scalp EEG signals and basal ganglia local field potentials (LFPs). This approach introduces the concept of movement-related poles, allowing one to study not only the classical event-related desynchronizations (ERD) and synchronizations (ERS), which correspond to modulations of power, but also event-related modulations of frequency. We applied the method to analyze movement-related EEG signals and LFPs contemporarily recorded from the sensorimotor cortex, the globus pallidus internus (GPi) and the subthalamic nucleus (STN) in a patient with Parkinson's disease who underwent stereotactic neurosurgery for the implant of deep brain stimulation (DBS) electrodes. In the AAR identification we compared the whale and the exponential forgetting factors, showing that the whale forgetting provides a better disturbance rejection and it is therefore more suitable to investigate movement-related brain activity. Movement-related power modulations were consistent with previous studies. In addition, movement-related frequency modulations were observed from both scalp EEG signals and basal ganglia LFPs. The method therefore represents an effective approach to the study of movement-related brain activity.

Algorithms↗

Noninvasive quantification of respiratory modulation on left ventricular size and stroke volume.

To noninvasively explore the complex interactions between heart rate, left ventricular (LV) stroke volume (SV) and respiration, different techniques were proposed and applied to the beat-to-beat measurements of end-diastolic (ED), end-systolic (ES) volumes and SV, obtained from echocardiographic acoustic quantification LV volume signal. Data were obtained from eight patients with dilated cardiomyopathy (DCM, age 60 +/- 2, mean +/- SE), and from 11 age-matched healthy volunteers (N, age 63 +/- 2). Spectral analysis showed an increase in HF power in DCM, both in ED (in ml2, 58 +/- 18 versus 19 +/- 5 in N) and in SV (in ml2, 55 +/- 12 versus 17 +/- 5 in N), together with an increase in total power (in ml2, ED: 119 +/- 31 in DCM versus 48 +/- 9 in N; SV: 88 +/- 17 in DCM versus 31 +/- 8 in N). Folded scattergrams evidenced higher amplitude oscillations in DCM (in ml, ES: 7.93 +/- 1.29; ED: 7.94 +/- 1.59; SV: 7.27 +/- 0.91) compared to N (in ml, ES: 4.08 +/- 0.64; ED: 3.56 +/- 0.65; SV: 3.63 +/- 0.43). Moreover, the prevalent effect of increased afterload generating SV reduction during inspiration was pointed out in N, while an intersubject dispersion in the relation between respiratory phase and LV dimension changes was found in DCM. Black-box model identification summarized these mechanisms by means of a few quantitative parameters.

Cardiomyopathy, Dilated↗

Biomedical signal processing and modeling in cardiovascular systems.

This article revisits the subject of short-term heart-rate and arterial-pressure variability from the perspective of model structures that can be useful in defining signal processing algorithms. We draw a general scheme of the oscillation sources and interactions that contribute to cardiovascular control mechanisms and highlight the elements that were considered in different modeling works. The origin, superposition, and interaction of respiratory high-frequency (HF) and vasomotor low-frequency (LF) rhythms is presented as the integration of supraspinal and spinal circuits, vasomotor activity, and pressure control loops. We analyze in detail the necessity of considering all relevant interactions for the algorithms designed to estimate the baroreflex sensitivity. We also pinpoint the components of cardiorespiratory coupling in relation to the analysis of data from the acoustic quantification of the left ventricular volume. Finally, we analyze the tendency to produce complex behaviors even in extremely simplified systems involving interactions between oscillatory mechanisms.

Animals↗