Applying Machine Learning for Human Seizure Prediction
We explore the use of both statistical and deep learning methods for the task of classifying iEEG signals as pre ictal or inter ical in order to perform human seizure prediction. Features were extracted that captured both the frequency and temporal properties of the raw data. The classifier performed adequately well by several performance metrics, especially given that the data set suffered from a significant class imbalance, with the majority of samples representing non seizure events. Thus, this paper demonstrates the feasibility of using a machine learning algorithm to accurately predict rare seizure events from a large set of EEG data. Such a model could have a significant impact on medicine, for its implementation on a medical device could predict and inform patients of seizure onset with a very high recall rate.