ANALYSIS OF ENTROPY DURING THE INTERICTAL AND ICTAL ELECTROCORTICOGRAM OF PATIENTS WITH REFRACTORY EPILEPSY INVESTIGATED WITH SUBDURAL GRIDS
Abstract number :
1.133
Submission category :
Year :
2002
Submission ID :
59
Source :
www.aesnet.org
Presentation date :
12/7/2002 12:00:00 AM
Published date :
Dec 1, 2002, 06:00 AM
Authors :
Ademar Pettri, Luis A. Diambra, Arthur Cukiert, Joaquim O. Vieira, Jose A. Burattini, Cassio R. Forster, Meire Argentoni, Cristine M. Baldauf, Valeria A. Mello, Carla Baise, Leila Frayman, Paulo T. Brainner-Lima. Instituto Ciencias Biomedicas, Universidad
RATIONALE: Approximate Entropy is a recently developed statistical measure of regularity and complexity. We applied the approximate entropy (ApEn) algorithm to further study and recognize epileptic seizures recorded in the chronic eletrocorticogram (ECoG) obtained through subdural electrode[ssquote]s grids.
METHODS: The interictal and ictal ECoG recordings of 3 patients with refractory epilepsy implanted with subdural electrode arrays were analyzed applying ApEn. Interictal activity containing no spikes was used as control for each individual. ApEn was applied using a slide window with N=300 (around 1sec of ECoG), with overlap of d=30 (samples), over a standard deviation (SD) of the signal. A filter r selected the frequency of presentation of vectors formed by two points, m=2 (m is the vector[ssquote]s dimension). The vectors were compared one to the other, measuring the maximum distance between their scalar components. Thirty epochs (30 sec duration) of epileptic activity were selected in each patient and mean and SD values for ApEn were calculated.
RESULTS: We got better results with r =1% or r=2%. Low entropy (below 0,50) was measured during the ictal onset and ensuing seizure in the three epileptic patients. Ictal onset entropy was 0,14[plusminus]0,026, 0,278[plusminus]0,022 and 0,136[plusminus]0,034 (mean[plusminus]SD) in each patient, respectively. Entropy levels above 0,5 were never recorded during ictal activity.
CONCLUSIONS: The ApEn algorithm is appropriate to evaluate non-stationary signals. It could detect modifications of complexity such as that seen during ictal onset. This is specially important while dealing with epileptic seizures in which long transients are difficult to obtain for spatial localization of epileptic foci. Additionally, it is conceivable that a fall in entropy may precede the actual ictal discharge or clinical seizure and might be used in the future in the very early detection of seizures and triggering of drug release or stimulatory treatments.
[Supported by: Sao Paulo Secretary of Health]