Open Database of Epileptic EEG with MRI and Postoperational Assessment of Foci—a Real World Verification for the EEG Inverse Solutions

This paper introduces a freely accessible database http://eeg.pl/epi, containing 23 datasets from patients diagnosed with and operated on for drug-resistant epilepsy. This was collected as part of the clinical routine at the Warsaw Memorial Child Hospital. Each record contains (1) pre-surgical electroencephalography (EEG) recording (10–20 system) with inter-ictal discharges marked separately by an expert, (2) a full set of magnetic resonance imaging (MRI) scans for calculations of the realistic forward models, (3) structural placement of the epileptogenic zone, recognized by electrocorticography (ECoG) and post-surgical results, plotted on pre-surgical MRI scans in transverse, sagittal and coronal projections, (4) brief clinical description of each case. The main goal of this project is evaluation of possible improvements of localization of epileptic foci from the surface EEG recordings. These datasets offer a unique possibility for evaluating different EEG inverse solutions. We present preliminary results from a subset of these cases, including comparison of different schemes for the EEG inverse solution and preprocessing. We report also a finding which relates to the selective parametrization of single waveforms by multivariate matching pursuit, which is used in the preprocessing for the inverse solutions. It seems to offer a possibility of tracing the spatial evolution of seizures in time

The role of network connectivity on epileptiform activity

A number of potentially important mechanisms have been identified as key players to generate epileptiform activity, such as genetic mutations, activity-dependent alteration of synaptic functions, and functional network reorganization at the macroscopic level. Here we study how network connectivity at cellular level can affect the onset of epileptiform activity, using computational model networks with different wiring properties. The model suggests that networks connected as in real brain circuits are more resistant to generate seizure-like activity. The results suggest new experimentally testable predictions on the cellular network connectivity in epileptic individuals, and highlight the importance of using the appropriate network connectivity to investigate epileptiform activity with computational models

Intracranial Volume Estimation and Graph Theoretical Analysis of Brain Functional Connectivity Networks

Understanding pathways of neurological disorders requires extensive research on both functional and structural characteristics of the brain. This dissertation introduced two interrelated research endeavors, describing (1) a novel integrated approach for constructing functional connectivity networks (FCNs) of brain using non-invasive scalp EEG recordings; and (2) a decision aid for estimating intracranial volume (ICV). The approach in (1) was developed to study the alterations of networks in patients with pediatric epilepsy. Results demonstrated the existence of statistically significant (

A hybrid unsupervised approach toward EEG epileptic spikes detection

Epileptic spikes are complementary sources of information in EEG to diagnose and localize the origin of epilepsy. However, not only is visual inspection of EEG labor intensive, time consuming, and prone to human error, but it also needs long-term training to acquire the level of skill required for identifying epileptic discharges. Therefore, computer-aided approaches were employed for the purpose of saving time and increasing the detection and source localization accuracy. One of the most important artifacts that may be confused as an epileptic spike, due to morphological resemblance, is eye blink. Only a few studies consider removal of this artifact prior to detection, and most of them used either visual inspection or computer-aided approaches, which need expert supervision. Consequently, in this paper, an unsupervised and EEG-based system with embedded eye blink artifact remover is developed to detect epileptic spikes. The proposed system includes three stages: eye blink artifact removal, feature extraction, and classification. Wavelet transform was employed for both artifact removal and feature extraction steps, and adaptive neuro-fuzzy inference system for classification purpose. The proposed method is verified using a publicly available EEG dataset. The results show the efficiency of this algorithm in detecting epileptic spikes using low-resolution EEG with least computational complexity, highest sensitivity, and lesser human interaction compared to similar studies. Moreover, since epileptic spike detection is a vital component of epilepsy source localization, therefore this algorithm can be utilized for EEG-based pre-surgical evaluation of epilepsy

Actas de SABI2020

Los temas salientes incluyen un marcapasos pulmonar que promete complementar y eventualmente sustituir la conocida ventilación mecánica por presión positiva (intubación), el análisis de la marchaespontánea sin costosos equipamientos, las imágenes infrarrojas y la predicción de la salud cardiovascular en temprana edad por medio de la biomecánica arterial