A novel reinforcement learning framework for online adaptive seizure prediction

Epileptic seizure prediction is still a very challenging and unsolved problem for medical professionals. The current bottleneck of seizure prediction techniques is the lack of flexibility for different patients with an incredible variety of epileptic seizures. This study proposes a novel self-adaptation mechanism which successfully combines reinforcement learning, online monitoring and adaptive control theory for seizure prediction. The proposed method eliminates a sophisticated threshold-tuning/optimization process, and has a great potential of flexibility and adaptability to a wide range of patients with various types of seizures. The proposed prediction system was tested on five patients with epilepsy. With the best parameter settings, it achieved an averaged accuracy of 71.34%, which is considerably better than a chance model. The autonomous adaptation property of the system offers a promising path towards development of practical online seizure prediction techniques for physicians and patients.