Abstract: Seizure onset can be forecast in subjects from subcutaneous electroencephalography (EEG) data input to a trained machine learning algorithm. A multi-stage training process is used to train the machine learning algorithm. A first stage of the training process is implemented on scalp-recorded EEG data. A second stage of the training process may be implemented on subcutaneously recorded EEG data.

Abstract: Occurrence of epileptic and other seizures are predicted or otherwise forecasted in ambulatory patients using a wrist-worn device and a trained machine learning algorithm. A multi-stage training process is used to train the machine learning algorithm. A first stage of the training process is implemented on EEG data obtained from bed-ridden, or otherwise non-ambulatory, subjects. A second stage of the training process may be implemented on EEG data obtained from ambulatory subjects. A third stage of the training process is implemented on a variety of data provided by a wrist-worn device. As an example, these data can include one or more of motion data (e.g., accelerometer data), skin temperature data, heart rate data, time of day, and so on. In some implementations, training data can be taken from early portions of each patient's wearable data, while testing results can be computed from the later portions, thereby skipping transfer learning steps.