Systems and methods for seizure prediction and detection

A method for seizure detection, comprising:

• (a) receiving a plurality of electroencephalography (EEG) signals over a plurality of channels for a subject;
• (b) preprocessing the plurality of EEG signals by segmenting the plurality of EEG signals for each channel into a plurality of temporal data segments;
• (c) extracting a plurality of features from each temporal data segment for each channel, wherein each temporal data segment is associated with a time epoch;
• (d) applying a machine learning algorithm to the plurality of features to perform a seizure binary classification for each temporal data segment for each channel, thereby generating a plurality of classifications for the plurality of temporal data segments, wherein the seizure binary classification for each temporal data segment comprises classifying each temporal data segment for each channel as (1) seizure-positive or (2) seizure-negative, wherein the plurality of classifications are compared sequentially across a plurality of time epochs on each channel, and a subset of the classifications is discarded if the subset comprises fewer than three seizure-positive classifications in a row;
• (e) aggregating the remaining non-discarded classifications for the plurality of temporal data segments for the plurality of channels over a moving time window;
• (f) determining a seizure burden for the moving time window based on the aggregated classifications, wherein the seizure burden comprises a percentage of the temporal data segments that are classified as seizure-positive, and wherein the seizure burden is a metric that provides a measure of a degree of severity or likelihood of a seizure;
• and
• (g) generating one or more notifications when the seizure burden is equal to or exceeds one or more thresholds, wherein the one or more notifications are indicative of different seizure activities and are useable for assessing whether the subject is at risk of having a seizure.