  Epileptic seizure forecasting, combined with the delivery of preventative
therapies, holds the potential to greatly improve the quality of life for
epilepsy patients and their caregivers. Forecasting seizures could prevent some
potentially catastrophic consequences such as injury and death in addition to a
long list of potential clinical benefits it may provide for patient care in
hospitals. The challenge of seizure forecasting lies within the seemingly
unpredictable transitions of brain dynamics into the ictal state. The main body
of computational research on determining seizure risk has been focused solely
on prediction algorithms, which involves a remarkable issue of balancing
accuracy and false-alarms. In this paper, we developed a seizure-risk warning
system that employs Bayesian convolutional neural network (BCNN) to provide
meaningful information to the patient and provide a greater opportunity for
him/her to be potentially more in charge of his/her health. We use scalp
electroencephalogram (EEG) signals and release information on the certainty of
our automatic seizure-risk assessment. In the process, we pave the ground-work
towards incorporating auxiliary signals to improve our EEG-based seizure-risk
assessment system. Our previous CNN results show an average AUC of 74.65% while
we could achieve on an EEG-only BCNN an average AUC of 68.70%. This drop in
performance is the cost of providing richer information to the patient at this
stage of this research.
