62 research outputs found
BrainNetVis: An Open-Access Tool to Effectively Quantify and Visualize Brain Networks
This paper presents BrainNetVis, a tool which serves brain network modelling
and visualization, by providing both quantitative and qualitative network measures
of brain interconnectivity. It emphasizes the needs that led to the creation of this
tool by presenting similar works in the field and by describing how our tool contributes
to the existing scenery. It also describes the methods used for the calculation
of the graph metrics (global network metrics and vertex metrics), which carry
the brain network information. To make the methods clear and understandable, we
use an exemplar dataset throughout the paper, on which the calculations and the
visualizations are performed. This dataset consists of an alcoholic and a control
group of subjects
Emerging and Established Trends to Support Secure Health Information Exchange
This work aims to provide information, guidelines, established practices and standards,
and an extensive evaluation on new and promising technologies for the implementation
of a secure information sharing platform for health-related data. We focus strictly on
the technical aspects and specifically on the sharing of health information, studying
innovative techniques for secure information sharing within the health-care domain,
and we describe our solution and evaluate the use of blockchain methodologically for
integrating within our implementation. To do so, we analyze health information sharing
within the concept of the PANACEA project that facilitates the design, implementation,
and deployment of a relevant platform. The research presented in this paper provides
evidence and argumentation toward advanced and novel implementation strategies
for a state-of-the-art information sharing environment; a description of high-level
requirements for the transfer of data between different health-care organizations or
cross-border; technologies to support the secure interconnectivity and trust between
information technology (IT) systems participating in a sharing-data “community”;
standards, guidelines, and interoperability specifications for implementing a common
understanding and integration in the sharing of clinical information; and the use of cloud
computing and prospectively more advanced technologies such as blockchain. The
technologies described and the possible implementation approaches are presented in
the design of an innovative secure information sharing platform in the health-care domain
DoctorEye: A clinically driven multifunctional platform, for accurate processing of tumors in medical images
Copyright @ Skounakis et al.This paper presents a novel, open access interactive platform for 3D medical image analysis, simulation and visualization, focusing in oncology images. The platform was developed through constant interaction and feedback from expert clinicians integrating a thorough analysis of their requirements while having an ultimate goal of assisting in accurately delineating tumors. It allows clinicians not only to work with a large number of 3D tomographic datasets but also to efficiently annotate multiple regions of interest in the same session. Manual and semi-automatic segmentation techniques combined with integrated correction tools assist in the quick and refined delineation of tumors while different users can add different components related to oncology such as tumor growth and simulation algorithms for improving therapy planning. The platform has been tested by different users and over large number of heterogeneous tomographic datasets to ensure stability, usability, extensibility and robustness with promising results. AVAILABILITY: THE PLATFORM, A MANUAL AND TUTORIAL VIDEOS ARE AVAILABLE AT: http://biomodeling.ics.forth.gr. It is free to use under the GNU General Public License
Parametric and Nonparametric EEG Analysis for the Evaluation of EEG Activity in Young Children with Controlled Epilepsy
There is an important evidence of differences in the EEG frequency spectrum of control subjects as compared to epileptic subjects. In particular, the study of children presents difficulties due to the early stages of brain development and the various forms of epilepsy indications. In this study, we consider children that developed epileptic crises in the past but without any other clinical, psychological, or visible neurophysiological findings. The aim of the paper is to develop reliable techniques for testing if such controlled epilepsy induces related spectral differences in the EEG. Spectral features extracted by using nonparametric, signal representation techniques (Fourier and wavelet transform) and a parametric, signal modeling technique (ARMA) are compared and their effect on the classification of the two groups is analyzed. The subjects performed two different tasks: a control (rest) task and a relatively difficult math task. The results show that spectral features extracted by modeling the EEG signals recorded from individual channels by an ARMA model give a higher discrimination between the two subject groups for the control task, where classification scores of up to 100% were obtained with a linear discriminant classifier
Review on solving the inverse problem in EEG source analysis
In this primer, we give a review of the inverse problem for EEG source localization.
This is intended for the researchers new in the field to get insight in the
state-of-the-art techniques used to find approximate solutions of the brain sources
giving rise to a scalp potential recording. Furthermore, a review of the performance
results of the different techniques is provided to compare these different inverse
solutions. The authors also include the results of a Monte-Carlo analysis which they
performed to compare four non parametric algorithms and hence contribute to what is
presently recorded in the literature. An extensive list of references to the work of
other researchers is also provided
A decision support framework for the discrimination of children with controlled epilepsy based on EEG analysis
This work was supported in part by the EC-IST project Biopattern, contract no:
508803, by the EC ICT project TUMOR, contract no: 247754, by the University of
Malta grant LBA-73-695, by an internal grant from the Technical University of
Crete, ELKE# 80037 and by the Academy of Finland, project nos: 113572,
118355, 134767 and 213462.Background: In this work we consider hidden signs (biomarkers) in ongoing EEG activity expressing epileptic
tendency, for otherwise normal brain operation. More specifically, this study considers children with controlled
epilepsy where only a few seizures without complications were noted before starting medication and who showed no
clinical or electrophysiological signs of brain dysfunction. We compare EEG recordings from controlled epileptic
children with age-matched control children under two different operations, an eyes closed rest condition and a
mathematical task. The aim of this study is to develop reliable techniques for the extraction of biomarkers from EEG
that indicate the presence of minor neurophysiological signs in cases where no clinical or significant EEG abnormalities
are observed.
Methods: We compare two different approaches for localizing activity differences and retrieving relevant information
for classifying the two groups. The first approach focuses on power spectrum analysis whereas the second approach
analyzes the functional coupling of cortical assemblies using linear synchronization techniques.
Results: Differences could be detected during the control (rest) task, but not on the more demanding mathematical
task. The spectral markers provide better diagnostic ability than their synchronization counterparts, even though a
combination (or fusion) of both is needed for efficient classification of subjects.
Conclusions: Based on these differences, the study proposes concrete biomarkers that can be used in a decision
support system for clinical validation. Fusion of selected biomarkers in the Theta and Alpha bands resulted in an
increase of the classification score up to 80% during the rest condition. No significant discrimination was achieved
during the performance of a mathematical subtraction task.peer-reviewe
Brain Network Analyzer
Brain Network Analyzer is an application, written in Java, that displays and analyzes synchronization networks from brain signals. The program implements a number of network indices and visualization techniques. The program has been used to analyze networks produced by electroencephalogram data of alcoholic and control patients
- …