Dynamics and Distant Effects of Frontal/Temporal Epileptogenic Focus Using Functional Connectivity Maps.

Objective: Connectivity patterns of interictal epileptiform discharges are all subtle indicators of where the three-dimensional (3D) source of a seizure could be located. These specific patterns are explored in the recorded electroencephalogram (EEG) signals of 20 individuals diagnosed with focal epilepsy to assess how their functional brain maps could be affected by the 3D onset of a seizure.

Methods: Functional connectivity maps, estimated by phase synchrony among EEG electrodes, were obtained by applying a data-driven recurrence-based method.

A Robust Edge Detection Approach in the Presence of High Impulse Noise Intensity through Switching Adaptive Median and Fixed Weighted Mean Filtering.

This study introduces a robust edge detection method that relies on an integrated process for denoising images in the presence of high impulse noise. This process is shown to be resilient to impulse (or salt and pepper) noise even under high intensity levels. The proposed switching adaptive median and fixed weighted mean filter (SAMFWMF) is shown to yield optimal edge detection and edge detail preservation, an outcome we validate through high correlation, structural similarity index and peak signal to noise ratio measures.

Connectivity maps of different types of epileptogenic patterns.

EEG functional connectivity maps, showing the interactions between brain areas in context to the placement of electrodes, were used for the investigation and comparison of three different types of epileptiform activity defined as single spike, spike followed by slow wave and repetitive spike. A nonlinear data-driven method was used to extract connectivity matrices that helped to identify network synchronization based on the number of connections for all brain regions, as represented by the 10-20 EEG system. This quantification was used to assess these three types of spike patterns in relation to the type of seizure, focal or generalized.

A probabilistic approach for pediatric epilepsy diagnosis using brain functional connectivity networks.

Background: The lives of half a million children in the United States are severely affected due to the alterations in their functional and mental abilities which epilepsy causes. This study aims to introduce a novel decision support system for the diagnosis of pediatric epilepsy based on scalp EEG data in a clinical environment.

Methods: A new time varying approach for constructing functional connectivity networks (FCNs) of 18 subjects (7 subjects from pediatric control (PC) group and 11 subjects from pediatric epilepsy (PE) group) is implemented by moving a window with overlap to split the EEG signals into a total of 445 multi-channel EEG segments (91 for PC and 354 for PE) and finding the hypothetical functional connectivity strengths among EEG channels.