Biomedical subjects
Publications and source records attributed to D Liberati.
Compartmental models are used for solving the problem of the control of dialysis therapy. The inadequacy of the existing monocompartmental model is faced, first with a careful analysis of the physiology of the system, then with a method focused on the construction of a new multicompartmental model. Moreover, impedance techniques allow us to solve the problem of measuring the total body water for each patient.
Pulsatile hormone secretion is usually investigated by measuring hormone concentration in samples of peripheral plasma. In this paper, the deconvolution of hormone time-series to reconstruct the instantaneous secretion rate of glands is considered. Various techniques are discussed and compared in order to overcome the ill-conditioning of the problem and reduce the computational burden. In particular, linear techniques based on least squares, maximum a posteriori (MAP) estimation, and Wiener filtering are compared. A new nonlinear MAP estimator that keeps into account the non-Gaussian distribution of the unknown signal is worked out and shown to yield the best results. The performances of the algorithms are tested on simulated time-series as well as on series of Luteinizing Hormone (LH).
A single trial technique has been used for the analysis of neuromagnetic signals. This method, applied to evoked magnetoencephalograms, permitted us to evaluate the dynamics of brain responses under repetitive somatosensory stimulation. Two different phenomena have been investigated: synchronized spontaneous activity (SSA), elicited by bursts of stimuli at a given frequency; and somatosensory evoked fields (SEF), the transient response to a single stimulus. A transient period of adaptation of the SSA response to the particular stimulation paradigm is reported at specific stimulation frequencies; no significant decrease of the response amplitude even after 300 repetitions is observed. The amplitudes of single SEFs also appear to be constant. Possible physiological implications of the SSA response parameters studied (amplitude, frequency and decay time), and a preliminary discussion of the relationship between SEF and SSA, are presented.
Single trial analysis of brain-evoked potentials via stochastic parametric identification and filtering is here extended to multichannel recordings, leading to the topographic mapping of the brain activity elicited by a single stimulus, instead of the usual averaged mapping. The temporal dynamics of the subsequent sweeps in the protocol of a neurophysiologic experiment can thus be recovered and quantified also on its spatial characteristic.
The paper introduces a Kalman filter procedure for the processing of single-sweep visual evoked potentials (VEPs). The identification of the filter coefficients is based on a model of signal and noise interaction which considers the generating process as the superposition of the true evoked response to an AR process (the background EEG) and a broader spectrum noise. Intersweep variability is thus evident on the filtered response and a functional parameter of the filter (VP(t), namely variability path) is proposed for the automatic determination of the latencies associated with the main peaks of the response. Finally, the time-variant algorithm allows the quantification of the intrasweep variability for possible interpretation of the physiological mechanism involved.
In this study, we used spectral analysis of short-term R-R and systolic arterial pressure (SAP) variabilities to estimate the changes in neural control of the circulation produced by psychological stress. The 0.1 Hz low-frequency (LF) component of R-R and SAP variabilities provided a quantitative index of the sympathetic activity controlling heart rate and vasomotion. Conversely the high-frequency (HF) respiratory component of R-R variability provided an index of vagal tone. In conscious dogs we used the seemingly stressful situation of being accompanied for the first time to the experimental laboratory as a stimulus. In human subjects we used mental arithmetic. In both cases LF of R-R and SAP variabilities increased significantly suggesting enhanced sympathetic activity both to the SA node and the vasculature. In man, the index alpha, a measure of the overall gain of baroreceptor mechanisms, was found to be reduced during mental arithmetic. Spectral analysis of cardiovascular variabilities thus suggests that in man and in conscious dogs psychological challenges induce a profound re-arrangement of neural control of the circulation, which appears to be characterised by sympathetic predominance and which can be monitored by this technique.
The study of the responsivity of the human brain at different rates of sensory stimulation has provided several pieces of information on brain functionality. The new analysis technique used in this study permits us to investigate the dynamics of these responses and to obtain a larger amount of statistical data to allow for fine frequency discrimination. This allows us to suggest, for example, phase locked systems as models for synchronisable spontaneous activity. Absence of subject fatigue during the repetition of the stimulation paradigm, as well as the presence of a 'learning period' characterised by transient dynamics of the responses, are also discussed.
This paper addresses the methodological problem of enhancing selective responses from the central nervous system when two (or more) different sensorial stimuli are simultaneously presented to the subject. In particular, contemporaneous visual and somato-sensory stimulation is considered and a model of signal and noise interaction is developed for the processing of the evoked responses. An ARXX parametric model (AutoRegressive with two eXogenous inputs) is introduced and a least squares algorithm is used to determine the selective response of the two neural systems from the overall evoked response. Such an analysis may be also carried out on a sweep-by-sweep basis. Applications of this method are the following ones: i) modeling of multisensory potentials; ii) description of facilitation or defacilitation phenomena in multitasking experiments; iii) analysis of cortico-cortical neural interactions.
We tested the hypothesis that psychological stress testing in the clinical laboratory provokes changes in the sympathetic and vagal activities regulating heart rate that can be assessed noninvasively using spectral analysis of RR variability. To account for the effects on respiration produced by talking, this study was performed with two different procedures: the I.K.T. (i.e., a computer-controlled mental task that is performed in silence and does not entail human confrontation) and a stressful interview. Finally, we assessed whether ischemic heart disease modifies the spectral changes induced by psychological stress by comparing a group of healthy subjects (age, 38 +/- 2 years) with a group of patients (age, 52 +/- 3 years) recovering from 1-month-old myocardial infarctions. The findings indicate that psychological stress induced marked changes in the sympathovagal balance, which moved toward sympathetic predominance. The low-frequency component of RR variability, a marker of sympathetic activity, increased from 58 +/- 5 normalized units (NU) to 68 +/- 3 NU with the I.K.T. and to 76 +/- 3 NU with the interview. This increase was absent in the group of post-myocardial infarction patients. However, arterial pressure increased significantly in both groups of subjects. The possibility of age playing an important role in determining the differences observed was disproved by the findings of a marked increase in low frequency with mental stimuli in an additional group of borderline hypertensive subjects with ages (55 +/- 2 years) comparable to those of post-myocardial infarction patients.
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Based on a model of signal-noise interaction, we present a method for single-sweep analysis of Visual Evoked Potentials. The EEG is represented as an autoregressive process and the single-sweep VEP as a filtered version of a reference signal taken as the running average of 20 consecutive sweeps. The algorithm for model identification and filtering is an ARX (AutoRegressive with eXogenous input) which provides a fast and efficient solution by means of a least squares approach. The choice of reference signal, as well as the complexity of the model, is also discussed. A further advantage of this approach is parameter reduction: all the single-sweep information is contained in 18 model coefficients and the reference signal.
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An original method is presented for the single sweep analysis of visual evoked potentials (VEP's). The introduced algorithm bases upon an AutoRegressive with eXogenous input (ARX) modeling. A Least Squares procedure estimates the coefficients of the model and allows to obtain a complete black-box description of the signal generation mechanism, besides providing a filtered version of the single sweep potential. The performance of the algorithm is verified on proper simulation tests and the experimental results put into evidence the noticeable improvement of signal-to-noise ratio with a consequent better recognition of the classical parameters of the peaks (latencies and amplitudes). The possibility of measuring these parameters on a single sweep basis enables to evaluate the dynamics of the Central Nervous System response during the entire course of the examination. A classification of the estimated evoked potentials in a small number of subsets, on the basis of their morphology, is also possible.
We introduce a method for processing visual evoked potentials, on the basis of a Wiener filter algorithm applied to a small number of consecutive responses. The transfer function of the filter is obtained by taking into account both the average of 99 sweeps (as an estimate of the true signal) and the EEG signal just before the stimulus onset (as an estimate of the noise superimposed on each individual response). The process acts as a sweep-by-sweep filter (in the sense of the mean square error) which considers the possible non-stationarities of the EEG signal during a complete clinical procedure. The average of a small number of consecutive filtered sweeps reveals variations in the morphology of the evoked responses which produce a change in the principal latencies. Applications are foreseen in neurophysiological studies of visual evoked potential responses, and in the clinic, where it is important to evaluate adaptive mechanisms, dynamic changes in single groups of visual evoked potentials and cognitive responses.
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A parametric method for autoregressive (AR) auto- and cross-spectral analysis is presented for the contemporaneous processing of heart rate and arterial blood pressure variability signals. In particular, the introduced bivariate spectral analysis (phase and coherence spectra) provides quantitative and objective means which are useful to measure the role played by the neural controlling systems (sympathetic and parasympathetic systems) on the cardiovascular signals under different pathophysiological conditions. Algorithmic aspects, connected to the way of processing discrete numerical series synchronized to single cardiac beats, are particularly stressed. Important applications are foreseen both in physiological studies and in clinical practice as an aid to the detection of various relevant cardiovascular pathologies such as hypertension and diabetes.
A procedure is described which aims to classify an EEG recorded during neurosurgery, for example intracerebral aneurysm clipping. A parametric approach is used; it employs auto-regressive (AR) modelling and Kalman filtering to quantify directly the dynamics of the EEG generating mechanism, supposing it to be a linear, time-invariant system driven by white noise. The results of this EEG processing are analysed together with simultaneous values of arterial blood pressure (ABP) as surgery of this kind is carried out under conditions of controlled hypotension. The object is to compare the sensitivity of ABP data with that obtained from the EEG and so provide an early warning of a potentially dangerous non-physiological state induced by the hypotensive drug (in this case sodium nitroprusside). Some methodological comments on the correct implementation of these algorithms are given and the procedure is compared with similar approaches which have appeared in the literature during the last few years. Particular emphasis is placed on the power spectral analysis of the signal by pointing out a method for spectral decomposition, related to AR power density estimation, which permits the separation of single spectral components in terms of central frequencies and their associated power. Other potential applications of this method are in long term EEG monitoring for the detection of changes due for example to drug infusion, to fast transient events, or to changes in the stationary condition.