Computer-based systems and devices configured for deep learning from sensor data non-invasive seizure forecasting and methods thereof

A system comprising:

• at least one sensor;
• and
• at least one processor in communication with the at least one sensor and configured to perform steps of instructions stored in a non-transitory memory, the steps comprising:
• receive from the at least one sensor at least one data stream associated with a user;
• wherein the at least one data stream comprises biomarker data parameters;
• utilize seizure forecasting machine learning model to predict a pre-ictal period probability associated with a forecasted time segment based at least in part on values of the at least one data stream;
• determine a segment value for an integration window of a history pre-ictal period probabilities for the forecasted time segment and at least one previously forecasted time segment;
• determine a pre-ictal period based at least in part on the segment value exceeding a pre-ictal probability threshold;
• determine a pre-ictal risk indication including a seizure treatment administration responsive to the pre-ictal risk indication;
• and
• cause to produce a pre-ictal risk indication at a computing device associated with the user to indicate a predicted risk of a seizure.

A method comprising:

• receiving, by at least one processor, a training dataset from a plurality of ground-truth time-series electrophysiological datasets;
• wherein each ground-truth time-series electrophysiological data of the plurality of ground-truth time-series electrophysiological datasets comprises a series of labelled epochs;
• determining, by the at least one processor, an epoch average of electrophysiological data values in each labelled epoch of each series of labelled epochs of each ground-truth time-series electrophysiological data;
• training, by the at least one processor, a seizure forecasting machine learning model using leave-one-out cross validation with of the training datasets based on labels associated with each labelled epoch and the epoch average associated with each labelled epoch;
• wherein machine learning model is trained on data from single or multiple patients to predict a pre-ictal period probability associated with a forecasted time segment based at least in part on values of the at least one data stream;
• wherein optimal values for integration window, a time span of the forecasted time segment, a pre-ictal probability threshold, a seizure occurrence period, or combinations thereof are determined by a leave-one-out cross-validation approach or are set according to individual preference;
• storing, by the at least one processor, the regression machine learning model in a memory upon being trained to predict the pre-ictal period probability.