Federico Lombardi

Heart Rate Variability as an Index of Sympathovagal Interaction After Acute Myocardial Infarction

By analysis of spectral components of heart rate variability, sympathovagal interaction was assessed in patients after acute myocardial infarction (AMI). At 2 weeks after AMI (n = 70), the low-frequency component was significantly greater (69 +/- 2 vs 53 +/- 3 normalized units [NU], p less than 0.05) and the high-frequency component was significantly smaller (17 +/- 1 vs 35 +/- 3 NU) than in 26 age-matched control subjects. This difference was likely to reflect an alteration of sympathovagal regulatory outflows with a predominance of sympathetic activity. At 6 (n = 33) and 12 (n = 29) months after AMI, a progressive decrease in the low- (62 +/- 2 and 54 +/- 3 NU) and an increase in the high-frequency (23 +/- 2 and 30 +/- 2 NU) spectral components was observed, which suggested a normalization of sympathovagal interaction. An increase in sympathetic efferent activity induced by tilt did not further modify the low-frequency spectral component (78 +/- 3 vs 74 +/- 3 NU) in a subgroup of 24 patients at 2 weeks after AMI. Instead, 1 year after AMI, this maneuver was accompanied by an increase in the low-frequency component (77 +/- 3 vs 53 +/- 3 NU, p less than 0.05) of a magnitude similar to the one observed in control subjects (78 +/- 3 vs 53 +/- 3 NU). These data indicate that the sympathetic predominance that is detectable 2 weeks after AMI is followed by recovery of vagal tone and a normalization of sympathovagal interaction, not only during resting conditions, but also in response to a sympathetic stimulus.

Conditional entropy approach for the evaluation of the coupling strength

Abstract. 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.

Significance of GPR polarisation for improving target detection and characterisation

This paper focuses on the application of ground penetrating radar (GPR) technique for civil engineering purposes, addressing the issues related to wave polarisation and antennas geometry. Even if polarisation of GPR signal is often an underestimated feature during data analysis and post processing, detection or avoidance of a specific target can be managed handling its polarimetric response. This opportunity is of high importance in this field of application, where the mixture of target with different polarimetric response is a commonly encountered situation. To provide an insight of this, two multicomponent GPR surveys have been performed: a first survey to show the effect of antenna-target mutual alignment variation and a second experiment in which the benefits of acquiring with different antenna arrangements are clearly evident. Because each antenna arrangement is sensitive towards different features of the received wavefield, this strategy is able to discriminate targets depending on their geometrical shape, thus delivering better detailed image of the acquired area.

Combining orthogonal polarization for elongated target detection with GPR

For an accurate imaging of ground penetrating radar data the polarization characteristics of the propagating electromagnetic (EM) wavefield and wave amplitude variations with antenna pattern orientation must be taken into account. For objects that show some directionality feature and cylindrical shape any misalignment between transmitter and target can strongly modify the polarization state of the backscattered wavefield, thus conditioning the detection capability of the system. Hints on the depolarization can be used to design the optimal GPR antenna survey to avoid omissions and pitfalls during data processing.This research addresses the issue of elongated target detection through a multi azimuth (or multi polarization) approach based on the combination of mutually orthogonal GPR data. Results from the analysis of the formal scattering problem demonstrate how this strategy can reach a scalar formulation of the scattering matrix and achieve a rotational invariant quantity. The effectiveness of the algorithm is then evaluated with a detailed field example showing results closely proximal to those obtained under the optimal alignment condition: detection is significantly improved and the risk of target missing is reduced.

Utilities detection through the sum of orthogonal polarization in 3D georadar surveys

Ground penetrating radar (GPR) is widely used in subsurface investigations for extracting the position and the route followed by the utility, an issue that gains more and more importance when considering the cost related to trench damage and disruptions. However, it has been noted that various targets of GPR surveys, especially linear and elongated targets, have polarization-dependent scattering characteristics. This implies that the visibility of a subsurface scatterer in the acquired data depends on the used antenna configuration and its orientation with respect to the feature to be imaged. Furthermore, wave attributes could be modified by the surrounding soil anisotropy and heterogeneity degree. As the GPR antennas are composed of directional dipoles, any changes in the propagation plane of the returning wave affects the recording of GPR data. This work presents an approach based on a combination of mutually orthogonal GPR 3D data volumes through which polarization issues can be overcome, ensuring target detection even when the position and material are adverse. The strategy is evaluated through two field examples: in homogeneous soil this technique fully recovers the polarization mismatch, providing results that are closely similar to the ones that would be obtained with the optimal configuration; in heterogeneous environments it overcomes the wavelet alteration, depolarization included, strongly enhancing the signal to noise ratio and improving target reconstruction.

Landmine detection from GPR data using convolutional neural networks

The presence of buried landmines is a serious threat in many areas around the World. Despite various techniques have been proposed in the literature to detect and recognize buried objects, automatic and easy to use systems providing accurate performance are still under research. Given the incredible results achieved by deep learning in many detection tasks, in this paper we propose a pipeline for buried landmine detection based on convolutional neural networks (CNNs) applied to ground-penetrating radar (GPR) images. The proposed algorithm is capable of recognizing whether a B-scan profile obtained from GPR acquisitions contains traces of buried mines. Validation of the presented system is carried out on real GPR acquisitions, albeit system training can be performed simply relying on synthetically generated data. Results show that it is possible to reach 95% of detection accuracy without training in real acquisition of landmine profiles.

Preliminary results on multi offset GPR for imaging of landmines

Ground Penetrating Radar (GPR) is widely recognised as an operationally useful sensor for mine detection as it can offer better detection performance than the ubiquitous metal detector in the presence of low-metal content mines. However, GPR has to overcome many potential sources of false alarm due to clutter and battlefield debris, which lower the efficiency of the sensor. This paper analyses a set of experimental data collected in a recent multi-offset GPR measurement campaign with inert landmines composed of different assemblies buried in sandy soil. The aim of the work is to evaluate the key differences observed by a radar system when the transmitter and the receiver are moved apart, as a function of their distance and hence when the illuminated section of the target is diversified. The results of the comparison between the collected multi-offset profiles show that using a bistatic geometry could represent a strategy to reconstruct composite objects with finer and better details.