F. Vatta

Improving lesion conductivity estimate by means of EEG source localization sensitivity to model parameter

Summary EEG-based source localization techniques use scalp-potential data to estimate the location of underlying neural activity. EEG source location reconstruction requires the assumption of a source model and the assumption of a conductive head model. Brain lesions can present conductivity values that are dramatically different from those of surrounding normal tissues and have to be included in head models for accurate neural source reconstruction. It is therefore necessary to analyze subjects’ anatomic images (using MRI or computed tomography) to identify lesion type and to assign the appropriate conductivity value. Source localization accuracy may be influenced by uncertainties in tissue conductivity assignment during head model construction. The authors present a sensitivity study quantifying the effect of uncertainty in brain lesion conductivity assignment on EEG dipole source localization. They adopted an eccentric-spheres head model in which an eccentric bubble approximated the effects of actual brain lesions. After simulating EEG signal measurement in 64 different pathologic situations, an inverse dipole fitting procedure was carried out, assuming an incorrect lesion conductivity assignment ranging from a half to twice the real value. Incorrect lesion conductivity assignment led to markedly wrong source reconstruction for highly conductive lesions like liquid-filled ones (localization errors as much as 1.7 cm). Conversely, low sensitivity to uncertainties in conductivity assignment was found for lesions with low conductivity like calcified tumors. The authors propose a method based on residual error analysis to improve the lesion conductivity estimate. This procedure can identify lesion tissue conductivity with only a few percent error and guarantees source localization errors less than 5 mm.

O3-DPACS Open-Source Image-Data Manager/Archiver and HDW2 Image-Data Display: An IHE-compliant project pushing the e-health integration in the world

After many years of study, development and experimentation of open PACS and Image workstation solutions including management of medical data and signals (DPACS project), the research and development at the University of Trieste have recently been directed towards Java-based, IHE compliant and multi-purpose servers and clients. In this paper an original Image-Data Manager/Archiver (O3-DPACS) and a universal Image-Data Display (HDW2) are described. O3-DPACS is also part of a new project called Open Three (O3) Consortium, promoting Open Source adoption in e-health at European and world-wide levels. This project aims to give a contribution to the development of e-health through the study of Healthcare Information Systems and the contemporary proposal of new concepts, designs and solutions for the management of health data in an integrated environment: hospitals, Regional Health Information Organizations and citizens (home-care, mobile-care and ambient assisted living).

Multiregion bicentric-spheres models of the head for the simulation of bioelectric phenomena

Equations are derived for the electric potentials [electroencephalogram (EEG)] produced by dipolar sources in a multiregion bicentric-spheres volume-conductor head model. Being the equations valid for an arbitrary number of regions, our proposal is a generalization of many spherical models presented so far in literature, each of those regarded as a particular case of our multiregion model. Moreover, our approach allows considering new features of the head volume-conductor to better approximate electrical properties of the real head.

A customizable game engine for mobile game-based learning

The use of computers in education has greatly increased during the last two decades. At the same time, technology advances have opened new spaces and possibilities for the field of computer-based edutainment-education in the form of entertainment — where learners can achieve their learning goals while having fun. Games on mobile phones have become a significant part of the contemporary culture experienced by young people. Research indicates the potential of mobile games to encourage learning in young adults. The 3-year EC-supported project mGBL (mobile Game-Based Learning) had the objective to prototype a platform for the development and deployment of mobile learning and guidance games, able to support the learning process and the support of decision making in critical situations not only in a cognitive but also in an emotional way. This paper describes key issues emerged in development phase of the Mogabal game engine within mGBL framework in both technical and pedagogical aspects, showing technologies, strategies and methodology adopted. A number of game prototypes based on such engine were devised and some were tested during the project user-trials. These games prototypes demonstrate the capabilities of the devised engine to cover a wide range of different games types and educational contents.

Modelling alarm management workflow in healthcare according to IHE framework by coloured Petri Nets

Ensuring patient safety in medical device networks by managing alarms and related clinical data is a life-critical issue. The Integrating the Healthcare Enterprise (IHE) initiative emerged to discuss and solve the interoperability and integration problems among medical information systems, vendors and users in order to improve patient care and healthcare system dependability. This paper models and analyzes the IHE Alarm Communication Management by using the Unified Modelling Language and Coloured Timed Petri nets. Aiming at generality, the model does not refer to a specific healthcare context but it is based on a general scheme where the message transactions are integrated with the nurse responses. In order to show the potentialities of the model, a real case study is simulated and different scenarios with different levels of workload are analyzed. The results illustrate that the model is able to provide support for structured and comprehensive analysis of the healthcare system management.

A FDM anisotropic formulation for EEG simulation

Accurate head modeling is required to properly simulate bioelectric phenomena in 3-D as well as to estimate the 3-D bioelectric activity starting from superficial bioelectric measurements and 3-D imaging. Aiming to build an accurate and realistic representation of the volume conductor of the head, also the anisotropy of head tissues should be taken into account. In this paper we describe a new finite-difference method (FDM) formulation which accounts for anisotropy of the various head tissues. Our proposal, being based on FDM, derives the head model directly from patients specific clinical images. We present here the details of the numerical formulation and the method validation by comparing our numerical proposal and known analytical results using a multi-shell anisotropic head model with skull anisotropy. Furthermore, we analyzed also different numerical grid refinement and EEG source characteristics. The comparison with previously developed FDM methods shows a good performance of the proposed method

Referenced EEG and head volume conductor model: geometry and parametrical setting

Brain electrical activity effects spread (spatially) over the whole head volume conductor. Electric scalp potentials (EEG) are the measurable evidences of such activity. EEG forward problem solution involves computing the scalp potentials at a finite set of sensor locations for a source configuration in a specified volume conductor model of the head or of part of it (reduced model). The use of reduced models is appealing for computational reasons. The skull could be, according to its conductivity, the natural bound for bioelectrical currents flow. However there are huge uncertainties on actual skull conductivity (i.e., 1/80 or 1/15 of brain conductivity). We show here the limits besides which model reduction is possible preserving EEG simulation accuracy according to the two competitive definitions for skull conductivity. The identified limits involve a proper choice for the EEG reference (Cz) as well as dipole equivalent source characteristics (position and orientation). To this end we adopted realistic test head models extended to different percentages of two reference models (extended to the chin) which differ for skull conductivity set either to 1/80 or 1/15 of brain conductivity.

Three-Dimensional Finite-Difference EEG forward problem solution on High Performance Computers

EEG forward problem solution using numerical head models with the same resolution and geometry as that available from MRI is desirable. This implies dealing with realistic head models of over 2 million elements, for which problem solution has so far been impractical due to issues of computation time and memory. This paper investigates the possibilities given by high performance computing (HPC) to obtain efficient EEG forward problem solution with high resolution head models of over 2 million elements at reduced computation time. In this paper, a finite difference forward problem solution based on HPC is proposed and tested with parallel implementations of different complexity. Solution feasibility with different HPC schemes is analyzed to individuate, by cost-effectiveness, the most appropriate system configurations allowing the best performances. Results indicate that a feasible solution based on a cluster of 8 processors is convenient, obtaining computation times not higher than 2 minutes. Increasing the cluster size above 32 processors gives no significant improvement in the computation times

Skull conductivity and extension of head volume conductor model: simulation of bioelectric phenomena

Brain electrical activity is spatially distributed over three dimensions and actually spreads within the whole volume conductor. However, under certain circumstances, it is possible to limit the volume within which the study can be done. Given its high resistivity, the skull limits the spread of bioelectrical currents due to brain sources and it leaves only few holes for current flow. A simulation study performed adopting realistic head models is here presented. Test models were extended to different percentages of the reference volume conductor model (extended to the chin). Skull conductivity was set either 1/80 or 1/15 of brain conductivity. Scalp potential distributions were compared between each reduced model and the reference model by means of relative-difference measure (RDM). RDM obtained with the two skull/brain conductivity ratios were then compared to evaluate the role of skull electric property vs. skull morphology, for the definition of the required extension of the reduced model. The found differences increased non linearly with model volume reduction, dramatically augmenting as the lower skull was removed. The same model performed differently according to source position and orientation.

Design of a domain model for clinical engineering within the HL7 Reference Information Model

The Health Level 7 (HL7) Reference Information Model (RIM) was introduced as an object oriented information model to harmonize the definition of HL7 messages across different application domains. On the heels of HL7s successful version 2, the last version 3, including the RIM, which forms its centerpiece, has received significant attention, but it has in turn also been subjected to criticism, addressing important questions about the usability in specialist domains. The RIM defines ‘normative’ classes such as Act, Role, Entity, etc. each of which is associated with a rich stock of attributes. When the RIM is applied to a new domain, one then needs to select and code these attributes. This paper reports the exploratory efforts that have been made to evaluate the feasibility of representing clinical engineering information in the HL7 RIM, with the purpose of developing a new HL7 v3 RIM based domain information model (DIM) dedicated to clinical engineering. This paper describes specifically the domain information analysis and the modeling phases of the proposed clinical engineering DIM development, using the Unified Modeling Language (UML). The proposed approach follows the reuse of standard healthcare information models for representation of clinical engineering information model, basing on the UML extensibility mechanisms and providing several advantages such us tooling support, graphical notation, exchangeability, extensibility, etc., which are also deployable in the next generation of HL7 tools.