Hardware Implementation of a Performance and Energy-optimized Convolutional Neural Network for Seizure Detection
Simon Heller, Maria Hügle, Iman Nematollahi, Farrokh Manzouri, Matthias Dümpelmann, Andreas Schulze-Bonhage, Joschka Boedecker, Peter Woias
July, 2018
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.
Publication
In International Conference of the IEEE Engineering in Medicine and Biology Society 2018
![Iman Nematollahi](/authors/admin/avatar_hu09dce571d44096762e8e9de4733c53fe_4009676_270x270_fill_q75_lanczos_center.jpg)
PhD Student in Robot Learning
My research interests include robot learning, intuitive physics and self-supervised learning.