Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.3
3.4
Journals
Sensors
Special Issues
EEG and Wearable Sensors for Epilepsy
sensors-logo
Submit to Sensors Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

EEG and Wearable Sensors for Epilepsy

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (20 August 2022) | Viewed by 4530

Special Issue Editor

Dr. Matthias Dümpelmann

E-Mail Website
Guest Editor
Epilepsy Center, Medical Center, University of Freiburg, 79106 Freiburg, Germany
Interests: EEG; wearables; brain imaging; epilepsy monitoring; biosignals and medical images; software development

Special Issue Information

Dear Colleagues,

The continuous recording of biosignals creates a new window for the monitoring and treatment of patients suffering from epilepsy compared to the snapshots available during outpatient visits or in-hospital video EEG monitoring. Mobile EEG and wearables offer new options for the monitoring of seizure frequency and the warning of seizures. Their use is of high interest for the detection of seizure-associated risks to patients, for differential diagnoses of rare seizure types and the prediction of seizure-prone periods.

In addition to EEG, which is traditionally used in the diagnosis of epilepsy accelerometry, electromyography (EMG), heart rate and further autonomic parameters are recorded to capture seizure manifestations and daily live activities. Respective sensors are integrated into novel devices for home monitoring.

However, the continuous acquisition of multimodal sensor signals also comes with new challenges related to signal quality over long recording intervals, the usability of sensors and devices in the daily life of the patients and a high need for the automated (pre-) processing of the sensor data. Distant monitoring will require the establishment of new eco-systems for data transfer, analysis and storage considering high protection needs for health data and regulations for medical devices.

This Special Issue aims to present novel developments for sensors, automated signal processing, pattern recognition and the description of platforms to integrate the recordings for the clinical use of this unknown amount of novel sensor data.

Dr. Matthias Dümpelmann
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Home monitoring in epilepsy 
  • Mobile EEG 
  • Wearable 
  • Seizure detection and prediction 
  • Signal quality 
  • Platforms for wearable data

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

20 pages, 2601 KiB  
Article
Intra- and Inter-Subject Perspectives on the Detection of Focal Onset Motor Seizures in Epilepsy Patients
by Sebastian Böttcher, Elisa Bruno, Nino Epitashvili, Matthias Dümpelmann, Nicolas Zabler, Martin Glasstetter, Valentina Ticcinelli, Sarah Thorpe, Simon Lees, Kristof Van Laerhoven, Mark P. Richardson and Andreas Schulze-Bonhage
Sensors 2022, 22(9), 3318; https://doi.org/10.3390/s22093318 - 26 Apr 2022
Cited by 11 | Viewed by 3313
Abstract
Focal onset epileptic seizures are highly heterogeneous in their clinical manifestations, and a robust seizure detection across patient cohorts has to date not been achieved. Here, we assess and discuss the potential of supervised machine learning models for the detection of focal onset [...] Read more.
Focal onset epileptic seizures are highly heterogeneous in their clinical manifestations, and a robust seizure detection across patient cohorts has to date not been achieved. Here, we assess and discuss the potential of supervised machine learning models for the detection of focal onset motor seizures by means of a wrist-worn wearable device, both in a personalized context as well as across patients. Wearable data were recorded in-hospital from patients with epilepsy at two epilepsy centers. Accelerometry, electrodermal activity, and blood volume pulse data were processed and features for each of the biosignal modalities were calculated. Following a leave-one-out approach, a gradient tree boosting machine learning model was optimized and tested in an intra-subject and inter-subject evaluation. In total, 20 seizures from 9 patients were included and we report sensitivities of 67% to 100% and false alarm rates of down to 0.85 per 24 h in the individualized assessment. Conversely, for an inter-subject seizure detection methodology tested on an out-of-sample data set, an optimized model could only achieve a sensitivity of 75% at a false alarm rate of 13.4 per 24 h. We demonstrate that robustly detecting focal onset motor seizures with tonic or clonic movements from wearable data may be possible for individuals, depending on specific seizure manifestations. Full article
(This article belongs to the Special Issue EEG and Wearable Sensors for Epilepsy)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop