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Dr. Matthias Dümpelmann

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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

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Keywords

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

Published Papers (1 paper)

Research

20 pages, 2601 KiB

Open AccessArticle

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://0-doi-org.brum.beds.ac.uk/10.3390/s22093318 - 26 Apr 2022

Cited by 7 | Viewed by 2209

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 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)

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