Epileptic Seizure Prediction Using Big Data and Deep Learning: Toward a Mobile System

Abstract

Background

Seizure prediction can increase independence and allow preventative treatment for patients with epilepsy. We present a proof-of-concept for a seizure prediction system that is accurate, fully automated, patient-specific, and tunable to an individual's needs.

Methods

Intracranial electroencephalography (iEEG) data of ten patients obtained from a seizure advisory system were analyzed as part of a pseudoprospective seizure prediction study. First, a deep learning classifier was trained to distinguish between preictal and interictal signals. Second, classifier performance was tested on held-out iEEG data from all patients and benchmarked against the performance of a random predictor. Third, the prediction system was tuned so sensitivity or time in warning could be prioritized by the patient. Finally, a demonstration of the feasibility of deployment of the prediction system onto an ultra-low power neuromorphic chip for autonomous operation on a wearable device is provided.

Results

The prediction system achieved mean sensitivity of 69% and mean time in warning of 27%, significantly surpassing an equivalent random predictor for all patients by 42%.

Conclusion

This study demonstrates that deep learning in combination with neuromorphic hardware can provide the basis for a wearable, real-time, always-on, patient-specific seizure warning system with low power consumption and reliable long-term performance.

Highlights

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  We use deep learning and long-term neural data to develop an automated, patient-tunable epileptic seizure prediction system.

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  We deploy our prediction system on a low-power neuromorphic chip to form the basis of a wearable device.

Predicting and treating the debilitating seizures suffered by epileptic patients has challenged medical researchers for over fifty years. A new way forward was opened when Cook and colleagues, in 2013, collected a large longitudinal and continuous dataset recorded directly from patients' brains for one to three years. Harnessing the recent breakthroughs in deep learning techniques and in building specialized processing chips, we have demonstrated that seizures can now be predicted by a portable device. Our system automatically learns patient-specific pre-seizure signatures, and, in real time, warns of oncoming seizures.