A Comparison of Energy-Efficient Seizure Detectors for Implantable Neurostimulation Devices.

ABSTRACT:

Introduction

About 30% of epilepsy patients are resistant to treatment with antiepileptic drugs, and only a minority of these are surgical candidates. A recent therapeutic approach is the application of electrical stimulation in the early phases of a seizure to interrupt its spread across the brain. To accomplish this, energy-efficient seizure detectors are required that are able to detect a seizure in its early stages.

Methods

Three patient-specific, energy-efficient seizure detectors are proposed in this study: (i) random forest (RF); (ii) long short-term memory (LSTM) recurrent neural network (RNN); and (iii) convolutional neural network (CNN). Performance evaluation was based on EEG data (n = 40 patients) derived from a selected set of surface EEG electrodes, whic

SUBMITTER: Manzouri F 

PROVIDER: S-EPMC8934428 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature