3 research outputs found
Detection of pure epileptic transitory among gamma oscillatory activities in MEG signals by SVD, embedded on a partial dynamic reconfiguration
In this work we used the SVD to reconstruct pure epileptic transitory
activities for the definition of the accurate sources responsible of the
excessive discharge implied by the transitory hallmark activities in pharmaco
resistant epilepsy. We applied firstly an automatic detection of the local
peaks of the transitory activities through a thresholding the energy
distrubition, then we performed the SVD on the detectable transitory (300 ms
window) simulated events to recover only the non contaminated transitory
activities by gamma oscillations, we calculate the precision of the SVD to
evaluate the robustness of this technique in separation between transitory and
gamma oscillations. In second phase we applied the SVD on small window of
transitory activities from real MEG signal, we obtained dipolar topographic map
for the pure transitory activities, however the time excitation for the recover
of pure epileptic transitory activities among the gamma oscillations were very
heavy and hence we propose to integrate the SVD on a dynamic partial
reconfiguration. This integration were very useful, in fact we did obtained
about 27 Times faster in the SVD execution
Evaluation of techniques for predicting seizure Build up
The analysis of electrophysiological signal of scalp: EEG
(electroencephalography), MEG (magnetoencephalography) and depth (intracerebral
EEG) IEEG is a way to delimit epileptogenic zone (EZ). These epileptic signals
present two different activities (oscillations and spikes) which can be
overlapped in the time frequency plane. Automatic recognition of epileptic
seizure occurrence needs several preprocessing steps. In this study, we
evaluated two filtering techniques: the stationary wavelet transforms (SWT) and
the Despikifying in order to extract pre ictal gamma oscillations (bio markers
of seizure build up). Then, we used a temporal basis set of Jmail et al 2017 as
a preprocessing step to evaluate the performance of both technique. Moreover,
we used time-frequency and spatio-temporal mapping of simulated and real data
for both techniques in order to predict seizure build up (in time and space).
We concluded that SWT can detect the oscillations, but Despikyfying is more
robust than SWT in reconstructing pure pre ictal gamma oscillations and hence
in predicting seizure build up.Comment: 10 pages and 7 figure
Integration of stationary wavelet transform on a dynamic partial reconfiguration: case study separating preictal gamma oscillations from transitory activities for early build up epileptic seizure
To define the neural networks responsible of the epileptic seizure, we had to
study the electrophysiological signal in a proper way. The early recognition of
the seizure build up could also be defined through the time space mapping of
the preictal gamma oscillations. The electrophysiological signals present three
types of wave: oscillations, spikes, and a mixture of both. Recent studies
prove that spikes and oscillations should be separated efficiently to define
the accurate neural connectivity for each activity. However retrieving the
transitory activity is a sensitive task due to the frequency interfering
between the gamma oscillatory and the transitory activities. Many filtering
techniques are highlighted to ensure a good separation: reducing false
oscillations in the transitory activity and vis versa. However and for a big
data set, this separation necessitate a big consumption in time execution, this
constraint will be overcome using embedded architecture. The integration of
these filtering techniques would also lead to creating instantaneous monitoring
of the seizure recognition, build up and neurofeedback devices. We propose here
to implement the stationary wavelet transform as a convenient filtering
technique to keep only the preictal gamma oscillations on a partial dynamic
configuration, then we will use the same architecture to integrate the time
space mapping for an early recognition of the build up seizure. We proved a
faster recognition of the build up seizure through the non contaminated
preictal gamma oscillations time space mapping (about 40 times faster),
obtained by the integration of the wavelet transform.Comment: 14 pages, 9 figures and 3 table