Luca Mainardi

Analysis of Heart Rate Variability Using Time-Varying Frequency Bands Based on Respiratory Frequency

In this paper a methodological approach for the analysis of nonstationary heart rate variability (HRV) signals using time-varying frequency bands based on respiratory frequency is presented. Spectral analysis of HRV is accomplished by means of the Smoothed Pseudo Wigner Ville distribution. Different approaches to the definition of the low frequency (LF) and high frequency (HF) bands are considered which involve respiratory information, derived either from a respiratory signal or from the ECG itself. Results are presented which derive from recordings acquired during stress testing and induced emotion experiments.

Time-frequency analysis of biomedical signals:

In the last few years interest has been dedicated on the time-frequency analysis, especially in applications to biological signal processing. In fact, analysis in the frequency domain is a well standardized tool for the quantification of many clinical and physiological phenomena. The main limitation is constituted by the required stationarity of the signal in the analysis window, while a great number of biological situations are characterized instead by a dynamical evolution. In this paper the principal methods of time-frequency analysis are briefly described: the Cohen class of distributions, the wavelet transform and the recursive autoregressive estimation. Examples of application are shown on different clinical protocols: time-variant analysis of the heart rate variability signal during drug infusion, the evaluation of the dynamical power distribution in the electroencephalogram during surgery, the single sweep analysis of evoked potentials and the analysis of the magnetic resonance imaging spectroscopic signal. Advantages and disadvantages of the methods are highlighted, in particular we want to put into evidence how the different features of the presented procedures are able to enhance different characteristics of the signals.

Modeling and estimation of time-varying heart rate variability during stress test by parametric and non parametric analysis

A methodological framework for simulating real-like HRV during stress test with controlled spectral properties has been developed with the purpose to assess SPWVD and time-variant AR analysis. For each method results have been evaluated computing the estimation error for LF and HF components during all the test (mean error of the order of 10% in all case) and a direct comparison based on the correlation between estimate and original ACF yields very high values (rhoWV=0, 99 and rhoAR=0, 94). In real data analysis bothmethods highlightan inversion of the relative spectral balance around the apex of exercise which passes from LF to HF prevalence.

Time-variant closed-loop interaction between HR and ABP variability signals during syncope

The mutual interactions existing between heart rate (HR) and arterial blood pressure (ABP) variability signals are investigated by means of a bivariate, time-variant model. The linear, parametric, closed-loop model, chosen to describe the relationships between HR and ABP spontaneous variability, is updated recursively according to the dynamic variations in the signals. In this way a beat-to-beat description of the interaction of the two signals and a beat-to-beat extraction of the relevant spectral and cross-spectral parameters are obtained. Furthermore, as a result of proper modelisation, a noninvasive measurement of the /spl alpha/-baroceptive gain is extracted on a beat-to-beat basis. The quantification of these parameters is obtained through an automatic spectral decomposition technique for an ARMA model. The parameters achieved from the proposed model are used to investigate episodes of vasovagal syncope. In particular the authors are interested in a quantitative assessment of the changes of the autonomic nervous system (ANS) behavior in the epoch preceding the episode and in the evaluation of ANS role in association with the vasovagal event.<<ETX>>

Improving PET image spatial resolution by experimental measurement of scanner blurring properties

This paper presents a method for the improvement of transaxial spatial resolution in positron emission tomography (PET) images in 2D acquisition mode. The method is based on the experimental measurement of the system blurring properties which are then inserted into an iterative reconstruction algorithm. In order to maintain acceptable the computation time, an ordered subset algorithm OSEM was used. The proposed algorithm was tested on different phantoms and on oncological patients. It showed good performances in terms of contrast improvement and partial volume recovery without increasing the image noise component. The application of the presented method is particularly important in whole body oncological studies, where there is the need to resolve small lesion which can be located at different distances from the scanner axis and in different blurring conditions.

Spectral Analysis of Cardiovascular Variability Signals

It is well known that the control mechanisms of heart rate and blood pressure, as well as of many other cardiovascular parameters, manifest themselves though beat-to-beat variations in the related signals1,2. The quantification of these variations, both in control conditions and after provocative tests, plays a fundamental role for a better comprehension of the pathophysiological properties of such mechanisms which act through neural, mechanical, vascular, humoral and other factors.

Applying 2D ML iterative reconstruction methods with resolution recovery to 3D PET data: evaluation of rebinning effects

The applicability of OSEM reconstruction algorithms with space dependent resolution recovery to clinical FDG-PET studies is verified. The performance of the 2D algorithm is improved by means of a low resolution initialization and by a infra-iteration Metz filtering. Effects of different rebinning algorithms on 3D data are assessed, concluding that they do not alter the transaxial plane blurring parameters, thus permitting a straightforward application of 2D OSEM reconstruction after rebinning, with the same system matrix. Finally axial degradation was also quantified, finding that FORE is the best rebinning method to be combined with the 2D OSEM reconstruction.

Multichannel time-variant spectral and cross-spectral parameters for the evaluation of cardiovascular variability signals before syncope

A beat-to-beat time-variant estimation of spectral (LF, HF and LF/HF ratio) and cross-spectral (phase and coherence functions) parameters is used to evaluate neural mechanisms of cardiovascular regulation before episodes of vaso-vagal syncope. The beat-to-beat parameters are obtained through a recursive identification of a bivariate model, which describe the mutual interactions between Heart Rate (HR) and Arterial Blood Pressure (ABP) variabilities. The obtained results evidence changes in neural regulation mechanisms, at level of both sinus node and vessels, in the epochs preceding syncope. Such changes may be linked to the successive haemodynamic alterations which lead to the syncopal event.