Wearable seizure detection using convolutional neural networks with transfer learning

The ability to accurately and robustly detect seizures in an ambulatory setting using scalp-based EEG has been the focus of much research over the last several decades. However, its numerous challenges and obstacles have impeded the realization of a definitive solution. This work aims to build upon our existing research and apply newer advanced machine learning and hardware techniques to this issue. The novelty proposed is two-fold. First, we utilize max-pooling convolutional neural networks (MPCNN) to perform end-to-end learning. Second, we demonstrate that transfer-learning can be used to teach MPCNNs generalized features of both normal and epileptiform patterns from a pool of subjects' raw EEG data. Using this hybrid approach, the system is able to detect all 184 seizure onsets from 24 cases with average latency of 1.47 seconds and 3.2 false-alarms/day. To demonstrate the full system, the entire design is efficiently implemented onto a highly parallel, highly granular embedded SoC (NVIDIA Jetson TK1). When utilizing the GPU, the system is able to classify 15-second segments in 308 μs and last over 80 hours.