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

Simon Heller; Maria Hugle; Iman Nematollahi; Farrokh Manzouri; Matthias Dumpelmann; Andreas Schulze-Bonhage; Joschka Boedecker; Peter Woias

doi:10.1109/EMBC.2018.8512735

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

Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul:2018:2268-2271. doi: 10.1109/EMBC.2018.8512735.

Authors

Simon Heller, Maria Hugle, Iman Nematollahi, Farrokh Manzouri, Matthias Dumpelmann, Andreas Schulze-Bonhage, Joschka Boedecker, Peter Woias

PMID: 30440858
DOI: 10.1109/EMBC.2018.8512735

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.

MeSH terms

Algorithms*
Electroencephalography*
Humans
Neural Networks, Computer*
Seizures* / diagnosis
Sensitivity and Specificity