  The time series captured by a single scalp electrode (plus the reference
electrode) of refractory epileptic patients is used to forecast seizures
susceptibility. The time series is preprocessed, segmented, and each segment
transformed into an image, using three different known methods: Recurrence
Plot, Gramian Angular Field, Markov Transition Field. The likelihood of the
occurrence of a seizure in a future predefined time window is computed by
averaging the output of the softmax layer of a CNN, differently from the usual
consideration of the output of the classification layer. By thresholding this
likelihood, seizure forecasting has better performance. Interestingly, for
almost every patient, the best threshold was different from 50%. The results
show that this technique can predict with good results for some seizures and
patients. However, more tests, namely more patients and more seizures, are
needed to better understand the real potential of this technique.
