Towards a wearable multi-modal seizure detection system in epilepsy: A pilot study

Jonas Munch Nielsen*, Ivan C Zibrandtsen, Paolo Masulli, Torben Lykke Sørensen, Tobias S Andersen, Troels Wesenberg Kjær

*Corresponding author af dette arbejde

Publikation: Bidrag til tidsskriftArtikelForskningpeer review


OBJECTIVE: To explore the possibilities of wearable multi-modal monitoring in epilepsy and to identify effective strategies for seizure-detection.

METHODS: Thirty patients with suspected epilepsy admitted to video electroencephalography (EEG) monitoring were equipped with a wearable multi-modal setup capable of continuous recording of electrocardiography (ECG), accelerometry (ACM) and behind-the-ear EEG. A support vector machine (SVM) algorithm was trained for cross-modal automated seizure detection. Visualizations of multi-modal time series data were used to generate ideas for seizure detection strategies.

RESULTS: Three patients had more than five seizures and were eligible for SVM classification. Classification of 47 focal tonic seizures in one patient found a sensitivity of 84% with a false alarm rate (FAR) of 8/24 h. In two patients each with nine focal nonmotor seizures it yielded a sensitivity of 100% and a FAR of 13/24 h and 5/24. Visual comparisons of features were used to identify strategies for seizure detection in future research.

CONCLUSIONS: Multi-modal monitoring in epilepsy using wearables is feasible and automatic seizure detection may benefit from multiple modalities when compared to uni-modal EEG.

SIGNIFICANCE: This study is unique in exploring a combination of wearable EEG, ECG and ACM and can help inform future research on monitoring of epilepsy.

Sider (fra-til)40-48
Antal sider9
TidsskriftClinical Neurophysiology
Tidlig onlinedato25 jan. 2022
StatusUdgivet - apr. 2022

Bibliografisk note

Copyright © 2022 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.


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