Early Seizure Detection with an Energy-Efficient Convolutional Neural Network on an Implantable Microcontroller
This addresses the need for implantable, closed-loop seizure detection systems for patients with severe epilepsy, representing an incremental improvement by optimizing existing methods for hardware constraints.
The paper tackled the problem of early seizure detection for implantable devices by developing an energy-efficient convolutional neural network optimized for ultra-low-power microcontrollers, achieving a median sensitivity of 0.96, false detection rate of 10.1 per hour, and median detection delay of 3.7 seconds.
Implantable, closed-loop devices for automated early detection and stimulation of epileptic seizures are promising treatment options for patients with severe epilepsy that cannot be treated with traditional means. Most approaches for early seizure detection in the literature are, however, not optimized for implementation on ultra-low power microcontrollers required for long-term implantation. In this paper we present a convolutional neural network for the early detection of seizures from intracranial EEG signals, designed specifically for this purpose. In addition, we investigate approximations to comply with hardware limits while preserving accuracy. We compare our approach to three previously proposed convolutional neural networks and a feature-based SVM classifier with respect to detection accuracy, latency and computational needs. Evaluation is based on a comprehensive database with long-term EEG recordings. The proposed method outperforms the other detectors with a median sensitivity of 0.96, false detection rate of 10.1 per hour and median detection delay of 3.7 seconds, while being the only approach suited to be realized on a low power microcontroller due to its parsimonious use of computational and memory resources.