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

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. (c) 2022 International Federation of Clinical Neurophysiology. Published by Elsevier B.V.

Automated summary

This study explores the potential of wearable multi-modal monitoring in epilepsy and identifies effective seizure detection strategies. Automatic seizure detection using multi-modal monitoring shows improved sensitivity and reduced false alarm rates. Visual analysis of multi-modal time series data generates insights for future research on seizure detection.