Epileptic Seizure Risk Assessment by Multi-Channel Imaging of the EEG
This work addresses seizure prediction for refractory epileptic patients, but it is incremental as it modifies an existing CNN approach by using likelihood averaging.
The paper tackled the problem of predicting epileptic seizures by using a CNN on EEG images to compute seizure likelihood, achieving higher sensitivity or lower false positive rates per hour compared to classification outputs.
Refractory epileptic patients can suffer a seizure at any moment. Seizure prediction would substantially improve their lives. In this work, based on scalp EEG and its transformation into images, the likelihood of an epileptic seizure occurring at any moment is computed using an average of the softmax layer output (the likelihood) of a CNN, instead of the output of the classification layer. Results show that by analyzing the likelihood and thresholding it, prediction has higher sensitivity or a lower FPR/h. The best threshold for the likelihood was higher than 50% for 5 patients, and was lower for the remaining 36. However, more testing is needed, especially in new seizures, to better assess the real performance of this method. This work is a proof of concept with a positive outlook.