Seizure prediction: A Visual Approach

Raul Molina, Andres Hernandez

Abstract:

Activity preceding the onset of epileptic seizures has
been an elusive subject for neuroscience research, without a clear
grasp of what patterns might be responsible. In this work, we
present an out of the box approach to this problem, trying to
mimic the visual inspection process that a trained physician
might do to locate the beginning of a pre-ictal state in an
EEG plot. We explore different data labeling methods for the
posterior training of a Convolutional Neural Network, taking
into account only visual characteristics for classification. Ten
second images (300x400 px) were synthesized from scalp EEG
recordings belonging to 10 epileptic patients from the public
Physionet CHB-MIT database. A tortuosity measure was taken
for each one-second window, for each channel (23 channels in
10-20 bipolar configuration). Unsupervised clustering methods
in conjunction with the mean and the standard deviation of the
tortuosity sets were used to identify pre-ictal states; interictal
states were selected according to the same proximity criteria
used for the Kaggles Melbourne University AES/MathWorks/NIH
Seizure Prediction Challenge. The proposed labelling method
indentified 28 posible pre-ictal states across 10 patients. Data
from pre-ictal states and interictal states was used to train ,
and test, a Convolutional Neural Network classifier for each of
the 8 patients selected. A classification accuracy of 99.29% was
achieved for the best patient; however, an accuracy of 46.93%
was also obtained for the worst patient. Mean performance across
patients was 76.03%, a 52.07% improvement over chance.