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


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.

In International Conference of the IEEE Engineering in Medicine and Biology Society 2018
Iman Nematollahi
Iman Nematollahi
PhD Student in Robot Learning

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