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Biomedical subjects

G Baselli

Publications and source records attributed to G Baselli.

At least 19 recordsLinked to original sources
PubMed2000

Evaluation of respiratory influences on left ventricular function parameters extracted from echocardiographic acoustic quantification.

This study was designed to assess, using the echocardiographic acoustic quantification technique, the influence of respiration on left ventricular (LV) function and its modifications connected with the ageing process, quantifying in a non-invasive way the respiratory contribution to the LV volume variability. An automated algorithm is applied to extract the beat-to-beat measurements of LV function parameters from the LV volume signal, obtained from recordings lasting a few minutes. Mean values, amount of variability and spectral content were studied in a population of 17 normal young (mean age 25 +/- 1 years) and 12 normal old (mean age 64 +/- 2 years) subjects. Mean values of the beat-to-beat measurements of LV function parameters were able to point out alterations connected with the ageing process in peak filling rate, peak atrial filling rate and peak ejection rate. Spectral analysis, applied to the extracted variability series, displayed a predominance of the high-frequency (HF) component corresponding to respiration in all LV function parameters; moreover, age related changes of HF variability were observed in peak ejection rate. The HF power spectrum component of beat to beat series extracted from the LV signal can provide a non-invasive assessment of the fluctuations in ventricular parameters associated with respiration.

Adult↗
PubMed2000

Assessment of arterial and cardiopulmonary baroreflex gains from simultaneous recordings of spontaneous cardiovascular and respiratory variability.

OBJECTIVES: In usual models of cardiovascular regulation, arterial pressure drives RR interval through a simple baroreflex, and the influence of respiration is dismissed. We examined the applicability of a trivariate autoregressive model to obtain separate values of the gain of the arterial and non-arterial, i.e. cardiopulmonary, components of the lumped baroreflex, employing spontaneous RR interval, systolic arterial pressure and respiration variability. DESIGN: We studied 30 normal subjects (age 37 +/- 1 years), both at rest and during standing, a condition known to enhance sympathetic activity while reducing venous return. Electrocardiogram was obtained by telemetry, arterial pressure by Finapres and respiration with a piezoelectric respiratory belt Data were acquired with a PC and processed with an ad hoc Windows program. METHODS: We employed an additive and a linear multivariate approach to approximate overall gain of the arterial pressure-heart beat period baroreflex (alphalumped) and of its arterial (alphaart) and non-arterial, i.e. cardiopulmonary (alphacp), components, from continuous beat-by-beat series of RR interval, systolic arterial pressure variability and respiration, without using any non-physiological intervention. RESULTS: The overall baroreflex gain at rest (alphalumped = 23.7 +/- 3.4 ms/mmHg) was subdivided into arterial (alphaart = 5.2 +/- 1.0 ms/mmHg) and cardiopulmonary (alphacp = 18.5 +/- 3.2ms/mmHg) components. During active orthostatism, alphaluumped was diminished to 10.0 +/- 2.2 ms/ mmHg. In addition, standing selectively reduced alphacp to 4.8 +/- 1.3 ms/mmHg, while alphaart was not significantly changed. CONCLUSIONS: A trivariate autoregressive model, that considers explicitly the influence of respiration, can subdivide overall, lumped, arterial pressure-heart period baroreflex gain, into two separate components, alphaart and alphacp. Only the latter is reduced by active orthostatism.

Adult↗
PubMed1999

Conditional entropy approach for the evaluation of the coupling strength.

A method that enables measurement of the degree of coupling between two signals is presented. The method is based on the definition of an uncoupling function calculating, by means of entropy rates, the minimum amount of independent information (i.e. the information carried by one signal which cannot be derived from the other). An estimator of the uncoupling function able to deal with short segments of data (a few hundred samples) is proposed, thus enabling the method to be used for usual experimental recordings. A synchronisation index is derived from the estimate of the uncoupling function by means of a minimisation procedure. It quantifies the maximum amount of information exchanged between the two signals. Simulations in which non-linear coordination schemes are produced and changes in the coupling strength are artificially induced are used to check the ability of the proposed index to measure the degree of synchronisation between signals. The synchronisation analysis is utilised to measure the coupling strength between the beat-to-beat variability of the sympathetic discharge and ventilation in decerebrate artificially ventilated cats and the degree of synchronisation between the beat-to-beat variability of the heart period and ventricular repolarisation interval in normal subjects and myocardial infarction patients. The sympathetic discharge and ventilation are strongly coupled and the coupling strength is not affected by manoeuvres capable of increasing or depressing sympathetic activity. The synchronisation is lost after spinalisation. The synchronisation analysis confirms that the heart period and ventricular repolarisation interval are well coordinated. In normal subjects, the synchronisation index is not modified by experimental conditions inducing changes in the sympathovagal balance. On the contrary, it strongly decreases after myocardial infarction, thus detecting and measuring the uncoupling between the heart period and ventricular repolarisation interval.

Animals↗
PubMed1998

Quantifying electrocardiogram RT-RR variability interactions.

A dynamic linear parametric model is designed to quantify the dependence of ventricular repolarisation duration variability on heart period changes and other immeasurable factors. The model analyses the beat-to-beat series of the RR duration and of the interval between R- and T-wave apexes (RT period). Directly from these two signals, a parametric identification procedure and spectral decomposition techniques allow RT variability to be divided into RR-related and RR-unrelated parts and allow the RT-RR transfer function to be calculated. RT variability is driven by RR changes at low frequency (LF, around 0.1 Hz) and high frequency (HF, at the respiratory rate), whereas, at very low frequencies, the RR-unrelated contribution to the total RT variability is remarkable. During tilt at LF the RR-related RT percentage power increases (p < 0.02), the RR-unrelated RT percentage power remains unchanged, the gain of the RT-RR relationship largely increases (p < 0.001), and the phase is not significantly modified. Both the RR-related and the RR-unrelated RT percentage powers at LF are not affected by controlled respiration, and an increase in the RT-RR gain at HF is observed (p < 0.02). The proposed analysis may help to describe the regulation of the ventricular repolarisation process and to extract indexes quantifying the coupling between heart period and ventricular repolarisation interval changes.

Electrocardiography↗
PubMed1998

Performance assessment of standard algorithms for dynamic R-T interval measurement: comparison between R-Tapex and R-T(end) approach.

Three automatic approaches to ventricular repolarisation duration measurement (R-Tapex, R-T(end threshold) and R-T(end fitting) methods) are compared on computer-generated and real ECG signals, in relation to their reliability in the presence of the most common electrocardiographic artefacts (i.e. additive broadband noise and additive and multiplicative periodical disturbances). Simulations permit the evaluation of the amount of R-T beat-to-beat variability induced by the artefacts. The R-T(end threshold) method performs better than the R-T(end fitting) one, and, hence, the latter should be used with caution when R-T(end) variability is addressed. Whereas the R-Tapex method is more robust with regard to broadband noise than the R-T(end threshold) one, the reverse situation is observed in the presence of periodical amplitude modulations. A high level of broadband noise dose not prevent the detection of the central frequency of underlying R-T periodical changes. Comparison between the power spectra of the beat-to-beat R-T variability series obtained from three orthogonal ECG leads (X,Y,Z) is used to assess the amount of real and artefactual variability in 13 normal subjects at rest. The R-Tapex series displays rhythms at high frequency (HF) with a percentage power on the Z lead (57.1 +/- 4.9) greater than that on the X and Y leads (41.9 +/- 4.6 and 46.1 +/- 4.9, respectively), probably because of respiratory-related artefacts affecting the Z lead more remarkably. More uniform HF power distributions over X,Y,Z leads are observed in the R-T(end threshold) series (31.8 +/- 3.8, 39.2 +/- 4.1 and 35.1 +/- 4.2, respectively), thus suggesting minor sensitivity of the R-T(end threshold) measure to respiratory-related artefacts.

Algorithms↗
PubMed1998

Measuring regularity by means of a corrected conditional entropy in sympathetic outflow.

A new method for measuring the regularity of a process over short data sequences is reported. This method is based on the definition of a new function (the corrected conditional entropy) and on the extraction of its minimum. This value is taken as an index in the information domain quantifying the regularity of the process. The corrected conditional entropy is designed to decrease in relation to the regularity of the process (like other estimates of the entropy rate), but it is able to increase when no robust statistic can be performed as a result of a limited amount of available samples. As a consequence of the minimisation procedure, the proposed index is obtained without an a-priori definition of the pattern length (i.e. of the embedding dimension of the reconstructed phase space). The method is validated on simulations and applied to beat-to-beat sequences of the sympathetic discharge obtained from decerebrate artificially ventilated cats. At control, regular, both quasiperiodic and periodic (locked to ventilation) dynamics are observed. During the sympathetic activation induced by inferior vena cava occlusion, the presence of phase-locked patterns and the increase in regularity of the sympathetic discharge evidence an augmented coupling between the sympathetic discharge and ventilation. The reduction of complexity of the neural control obtained by spinalization decreases the regularity in the sympathetic outflow, thus pointing to a weaker coupling between the sympathetic discharge and ventilation.

Animals↗
PubMed1998

Assessment of the coupling between RTapex and RR interval as an index of temporal dispersion of ventricular repolarization.

To evaluate the dynamic characteristics of the relationship between the RT and RR intervals we analyzed the RR/RTapex variability interaction with a dynamic parametric model whose parameters can be directly estimated from the beat-to-beat series RR and RTapex intervals. The model is designed to separate the fraction of RTapex variability driven by RR changes from that independent of RR variations and to quantify the gain and phase of the relationship between RR and RTapex intervals. The percentage of RTapex variability driven by RR variability was significantly greater in young normal subjects in comparison with postmyocardial infarction patients as well as with age-matched control subjects. This new approach based on the quantification of the RTapex variability dependent and independent of beat-to-beat RR interval changes could be used to quantify the degree of uncoupling between the two signals thus providing a new and noninvasive index of temporal dispersion of ventricular repolarization.

Adult↗
PubMed1997

Spectral decomposition in multichannel recordings based on multivariate parametric identification.

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.

Algorithms↗
PubMed1996

Classification of coupling patterns among spontaneous rhythms and ventilation in the sympathetic discharge of decerebrate cats.

The spontaneous low- and high-frequency rhythms in the sympathetic discharge of decerebrate artificially ventilated cats are affected by external ventilation. Two graphical methods (i.e. the space-time separation plot and the frequency tracking locus) are used to classify the non-linear interactions. The observed behaviours in the sympathetic discharge consist of phase-locked periodic dynamics (at various frequency ratios with ventilation), quasiperiodic and aperiodic patterns. They depend on the experimental condition. In control condition the sympathetic discharge appears more frequently locked to each ventilatory cycle (1:1 dynamics). However, some cases of quasiperiodic dynamics are found. A sympathetic activation stimulus, such as inferior vena cava occlusion, is able to synchronize slow rhythms in the sympathetic discharge to a subharmonic of ventilation. During a sympathetic inhibition stimulus, such as aortic constriction, 1:1 dynamics is detected but the amplitude of the sympathetic responses can be modulated by unlocked slow rhythms. Moreover some cases of aperiodic dynamics are observed. Vagotomy reduces the 1:1 coupling between sympathetic outflow and ventilation. Vagotomy plus spinalisation disrupts periodic dynamics in the sympathetic discharge so that irregular and complex patterns are found.

Animals↗
PubMed1996

Linear multivariate models for physiological signal analysis: theory.

The general linear parametric multivariate modelling concept is presented. This model combines a variety of different kinds of multivariate linear models. The concept of partial spectral analysis is derived from the general model. Some emphasis is laid on the causality demands of the model, and it is shown that the classic strictly-causal structure must be abandoned in order to utilise the modelling in many practical situations. Two special sub-class models are described in detail: the multivariate autoregressive model and the multivariate dynamic adjustment model. Furthermore, time-varying modelling is considered. The modelling of the real system is presented on a general level as a system identification cycle. The application of the methods to real physiological data is presented in the companion paper.

Data Collection↗
PubMed1996

Linear multivariate models for physiological signal analysis: applications.

Some applications of linear multivariate modelling methods in the analysis of physiological signals are presented. These applications illustrate the methods in the analysis of cardiovascular dynamics, which has been one of the main application fields of the multivariate modelling during the last ten years. It is demonstrated that physiologically meaningful information about the causal interactions in the cardiovascular system can be drawn from the routinely available clinical signals. Both static and dynamic conditions are considered.

Humans↗
PubMed1996

Spectral analysis of short term R-Tapex interval variability during sinus rhythm and fixed atrial rate.

Analysis of heart rate variability has been proven useful in stratifying post myocardial patients at risk and in evaluating autonomic dysfunction. Recently augmented inter-lead variability of the QT interval has been associated with increased mortality as a result of arrhythmia and proposed as a marker of dispersion of ventricular repolarization. As the duration of the QT interval is largely dependent upon the length of the preceding cardiac cycle it is tempting to analyse whether neural mechanisms might also directly exert additional modulation. Using autoregressive algorithms we therefore analysed RR and R-Tapex interval variabilities in 15 normal subjects during sinus rhythm and in six patients with a fixed atrial rate. In controls mean R-Tapex interval and variance measured on the vector magnitude were, respectively, 245 +/- 6 ms and 5.1 +/- 0.7 ms2. Spectral analysis of R-Tapex indicated the presence of two spectral components which corresponded to the low and high frequency components of heart rate variability. In R-Tapex variability, high frequency (44 +/- 4 nu) was predominant over low frequency (29 +/- 4 nu). During controlled respiration, a manoeuvre associated with enhanced vagal modulation of sinus node, there was a further increase in high frequency (58 +/- 4 nu) whereas during tilt the low frequency component of R-Tapex variability became predominant (57 +/- 6 nu). In patients with a fixed atrial rate, variance was extremely low (3 +/- 0.9 ms2) and only a respiration-related high frequency component was recognizable in spectral analysis of RR and R-Tapex variabilities. This component was likely to depend upon mechanically induced changes in cardiac vector orientation. These data indicate that during sinus rhythm short-term R-Tapex interval variability is characterized by the same rhythmical components present in RR variability. However, the presence of a very low variance and of only a high frequency component in patients in whom the physiological variability of sinus node is abolished by atrial pacing. suggests that neural modulatory mechanisms do not exert a direct effect on the length of the R-Tapex interval.

Adult↗
PubMed1995

Pole-tracking algorithms for the extraction of time-variant heart rate variability spectral parameters.

Various algorithms of autoregressive (AR) recursive identification make it possible to evaluate power spectral distribution in correspondence with each sample of a time series, and time-variant spectral parameters can be calculated through the evaluation of the pole positions in the complex z-plane. In traditional analysis, the poles are obtained by zeroing the denominator of the model transfer function, expressed as a function of the AR coefficients. In this paper, two algorithms for the direct updating and tracking of movements of poles of an AR time-variant model on the basis of the innovation given to the coefficients are presented and investigated. The introduced algorithms are based upon 1) the classical linearization method and 2) a recursive method to compute the roots of a polynomial, respectively. In the present paper, applications in the field of heart rate variability (HRV) signal analysis are presented and efficient tools are proposed for quantitative extraction of spectral parameters (power and frequency of the low-frequency (LF) and high-frequency (HF) components) for the monitoring of the action of the autonomic nervous system in transient patho-physiological events. These computational methods seem to be very attractive for HRV applications, as they inherit the peculiarity of recursive time-variant identification, and provide a more immediate comprehension of the spectral process characteristics when expressed in terms of poles and AR spectral components.

Algorithms↗
PubMed1994

Model for the assessment of heart period and arterial pressure variability interactions and of respiration influences.

A model which assesses the closed-loop interaction between heart period (HP) and arterial pressure (AP) variabilities and the influence of respiration on both is applied to evaluate the sources of low frequency (LF approximately 0.1 Hz) and high frequency (HF, respiratory rate approximately 0.25 Hz) in conscious dogs (n = 18) and humans (n = 5). A resonance of AP closed-loop regulation is found to amplify LF oscillations. In dogs, the resonance gain increases slightly during baroreceptor unloading (mild hypotension obtained with nitroglycerine (NTG) i.v. infusion, n = 8) and coronary artery occlusion ((CAO), n = 6), and it is abolished by ganglionic transmission blockade ((ARF), Arfonad i.v. infusion, n = 3). In humans, this gain is considerably increased by passive tilt. Different, possibly central, sources of LF oscillations are also evaluated, finding a strong rhythmic modulation of HP during CAO. At HF, a direct respiratory arrhythmia is dominant in dogs at control, while it is considerably reduced during CAO. On the contrary, in humans, a strong influence of respiration on AP is shown which induces a reflex respiratory arrhythmia. An index of the gain of baroreceptive response, alpha cl, was decreased by NTG and CAO, and virtually abolished by chronic arterial baroreceptive denervation (TABD, n = 4) and ARF.

Animals↗
PubMed1994

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

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 Poincaré 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.

Animals↗
PubMed1993

Body position affects the power spectrum of heart rate variability during dynamic exercise.

The power spectrum analysis of R-R interval variability (RRV) has been estimated by means of an autoregressive method in six men in supine (S) and sitting (C) postures at rest and during steady-state cycle exercise at about 14%, 28%, 45%, 67% of the maximal oxygen consumption (% VO2max). The total power of RRV decreased exponentially as a function of exercise intensity in a similar way in both postures. Three components were recognized in the power spectra: firstly, a high frequency peak (HF), an expression of respiratory arrhythmia, the central frequency (fcentral) of which increased in both S and C from a resting value of about 0.26 Hz to 0.42 Hz at 67% VO2max; secondly, a low frequency peak (LF) related to arterial pressure control, the fcentral of which remained constant at 0.1 Hz in C, whereas in S above 28% VO2max decreased to 0.07 Hz; and thirdly, a very low frequency component (VLF; less than 0.05 Hz, no fcentral). The power of the three components (as a percentage of the total power) depended on the body posture and the metabolic demand. HF% at rest was 30.3 (SEM 6.6) % in S and 5.0 (SEM 0.8) % in C. During exercise HF% decreased by about 30% in S and increased to 19.7 (SEM 5.5) % at 28% VO2max in C. LF% was lower in S than in C at rest [31.6 (SEM 5.7) % vs 44.9 (SEM 6.4) %; P < 0.05], remaining constant up to 28% VO2max.(ABSTRACT TRUNCATED AT 250 WORDS)

Adult↗
PubMed1993

Power spectrum analysis of cardiovascular variability monitored by telemetry in conscious unrestrained rats.

Beat-to-beat variability of arterial pressure and heart period (R-R) was studied in eight conscious freely-moving adult male rats in which telemetric recordings of arterial pressure, ECG and respiratory movements were obtained under unrestrained and unstressed conditions. The beat-to-beat time series of these signals (systolic arterial pressure, diastolic arterial pressure and R-R) were analyzed, in the frequency domain, using autoregressive spectral analysis in order to detect and quantify the rhythmic components. In basal conditions, the systolic arterial pressure variability spectrum was characterized by three major spectral components which had central frequencies respectively of 0.08 +/- 0.03 Hz (very low frequency), 0.43 +/- 0.02 Hz (low frequency) and 1.36 +/- 0.19 Hz (high frequency). Similar rhythmic components were found in R-R signal variability. The very low frequency component included a higher percentage of total power in R-R variability spectrum (75.3%) than in systolic arterial pressure variability spectrum (58.4%). The low frequency component was more pronounced in both systolic and diastolic arterial pressure variability spectra. The high frequency component of R-R, systolic and diastolic arterial pressure was synchronous with respiration. Cross-spectral analysis revealed a high statistical coherence between R-R and arterial pressure variabilities in all the three frequency bands. An alpha-adrenergic blocker (phentolamine) specifically abolished the low frequency components of systolic and diastolic arterial pressure variability spectra, thus suggesting that low frequency is a marker of sympathetic modulation of vasomotor activity. The low frequency component of R-R variability spectrum was also markedly blunted. We suggest that cardiovascular variability signals, (R-R, systolic and diastolic arterial pressure) are composed almost of two main rhythms linked to respiration and vasomotor activity. These rhythms can be quantified in conscious unrestrained rats by using telemetry and spectral analysis. This approach seems to offer a new powerful tool for pharmacological studies in conscious small animals.

Animals↗