Abstract: A computer implemented method of generating a custom signal from a data library containing multiple datasets of variable values correlated with time and geography includes receiving a user defined target variable, a time parameter, and a geography parameter, determining the applicable datasets from the data library overlapping the user-defined time parameter or geography parameter, testing the control variables of the applicable datasets for statistical significance to the target variable, aggregating a custom signal of at least three control variables having the greatest statistical significance to the target variable. The method includes generating a forecasting model by determining an internal feature analysis, determining an optimal external feature analysis, and selecting an optimal feature set based on a statistical strength of the internal feature analysis and the optimal external feature analysis.

Abstract: In some embodiments, an electrical probing stimulation pattern is delivered to the brain of a subject. A response to the electrical probing is analyzed, and used to determine a type of predicted seizure. The type of predicted seizure may be used to determine a treatment electrical stimulation pattern that may be administered to prevent onset of the predicted seizure. In some embodiments, a predicted seizure metric is calculated, which, in some implementations, acts as an indicator of “distance” (e.g., probability distance) to the predicted seizure. Furthermore, a subject model may be trained to assist with determining the type of predicted seizure, and determining the treatment electrical stimulation pattern.