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Many challenges exist in designing a reliable system for forecasting seizures from noninvasively recorded data. The power and capability of deep learning algorithms trained on very large datasets hold promise to enable applications not previously believed possible, and may open the door to seizure forecasting with noninvasive sampling devices.
AMBULATORY EEG SERIES
In particular these “end-to-end learning” methods are attractive for seizure forecasting given the challenges of identifying salient features in ultra-long term time-series data, and the heterogeneity in time series data characteristics between different patients. Deep learning approaches have shown promising performance for a variety of difficult applications 15, including seizure forecasting 7.
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Hence there is presently great interest in forecasting seizures using wearable or minimally invasive devices. However, invasive devices may not be acceptable for some patients with epilepsy, and no clinically available invasive device currently has the capability to sample and telemeter data needed for seizure forecasting. Accurate seizure forecasts have been demonstrated using invasively sampled ultralong-term EEG in ambulatory canine 6, 7, 8 and human subjects 9, 10, 11, 12, 13, 14, including a prospective study with a dedicated device 11. Reliable seizure forecasts could potentially allow people living with recurrent seizures to modify their activities, take a fast-acting medication, or increase neuromodulation therapy to prevent or manage impending seizures. People living with epilepsy consistently report the unpredictability of seizures to be the most limiting aspect of their condition 5. Half or more of patients who undergo resective surgery for epilepsy have eventual recurrence of seizures 1, 2, and devices for neuromodulation rarely achieve long-term seizure freedom 3, 4. Despite optimized medication therapy, resective surgery, and neuromodulation therapy, many people with epilepsy continue to experience seizures.