Hardware Implementation of a Performance and Energy-optimized Convolutional Neural Network for Seizure Detection

Abstract

We present for the first time a μW-power convolutional neural network for seizure detection running on a low-power microcontroller. On a dataset of 22 patients a median sensitivity of 100% is achieved. With a false positive rate of 20.7 fp/h and a short detection delay of 3.4 s it is suitable for the application in an implantable closed-loop device.

Iman Nematollahi

PhD Student in Robot Learning

My research interests include robot learning, intuitive physics and self-supervised learning.