Roughly 1% of the population lives with anxiety around their next seizure event. Anti-convulsant medication does not help at least 20% of those afflicted, but also carries negative side effects in high doses. This fear stands in the way of performing everyday tasks like driving a car or swimming. There exist devices that can detect that a seizure is occurring, but we aim to build a wearable EEG device that can predict that a seizure is about to occur so a caregiver and/or the user can pause what they are doing and take appropriate preventative action.
Five years ago, a Kaggle contest framed the challenge of developing a machine learning algorithm to help predict seizure events. Contestants were provided 106 GB of training data in.mat files. Each training sample represents 10 minutes of raw intracranial EEG readings. For 5 dogs, data included 16 channel readings sampled at 400 Hz. For 2 human patients, data was sampled at 5000 Hz over 24 channels.
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