Low-Latency Detection of Epileptic Seizures from iEEG with Temporal Convolutional Networks on a Low-Power Parallel MCU

Epilepsy is a severe neurological disorder that affects about 1% of the world population, and one-third of cases are drug-resistant. Apart from surgery, drug-resistant patients can benefit from closed-loop brain stimulation, eliminating or mitigating the epileptic symptoms. For the closed-loop to be accurate and safe, it is paramount to couple stimulation with a detection system able to recognize seizure onset with high sensitivity and specificity and short latency, while meeting the strict computation and energy constraints of always-on real-time monitoring platforms. We propose a novel setup for iEEG-based epilepsy detection, exploiting a Temporal Convolutional Network (TCN) optimized for deployability on low-power edge devices for real-time monitoring. We test our approach on the Short-Term SWEC-ETHZ iEEG Database, containing a total of 100 epileptic seizures from 16 patients (from 2 to 14 per patient) comparing it with the state-of-the-art (SoA) approach, represented by Hyperdimensional Computing (HD). Our TCN attains a detection delay which is 10 s better than SoA, without performance drop in sensitivity and specificity. Contrary to previous literature, we also enforce a time-consistent setup, where training seizures always precede testing seizures chronologically. When deployed on a commercial low-power parallel microcontroller unit (MCU), each inference with our model has a latency of only 5.68ms and an energy cost of only 124.5 mu J if executed on 1 core, and latency 1.46ms and an energy cost 51.2 mu J if parallelized on 8 cores. These latency and energy consumption, lower than the current SoA, demonstrates the suitability of our solution for real-time long-term embedded epilepsy monitoring.