ArticleSensors (Basel, Switzerland)2022
Intra- and Inter-Subject Perspectives on the Detection of Focal Onset Motor Seizures in Epilepsy Patients.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 3 of them syntheses that pooled it.
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Who cites it
13 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Automated seizure detection using wearable devices: updated systematic review and meta-analysis of tonic-clonic and focal seizure detection.Frontiers in bioengineering and biotechnology · 2026Pooled it
- Movement Disorders and Smart Wrist Devices: A Comprehensive Study.Sensors (Basel, Switzerland) · 2025Pooled it
- Feasibility of cardiac-based seizure detection and prediction: A systematic review of non-invasive wearable sensor-based studies.Epilepsia open · 2024Pooled it
- The research progress of wearable digital health technologies in epilepsy management.Acta epileptologica · 2026Review
- Near-Real-Time Epileptic Seizure Detection with Reduced EEG Electrodes: A BiLSTM-Wavelet Approach on the EPILEPSIAE Dataset.Brain sciences · 2026Article
- Wearable Artificial Intelligence for Epilepsy: Scoping Review.Journal of medical Internet research · 2025Article
- Detecting Diverse Seizure Types with Wrist-Worn Wearable Devices: A Comparison of Machine Learning Approaches.Sensors (Basel, Switzerland) · 2025Article
- Detection of focal impaired awareness seizures using a biometric shirt.Epilepsia open · 2025Article
- Early hemodynamic differences between generalized and focal epilepsy measured by photoplethysmography.Scientific reports · 2025Article
- Improving epilepsy monitoring using long-term, in-home-bi-modal seizure monitoring device: clinical utilities and obstacles from a pilot study.Frontiers in neurology · 2025Article
- Article
- Exploring Digital Biomarkers of Illness Activity in Mood Episodes: Hypotheses Generating and Model Development Study.JMIR mHealth and uHealth · 2023Observational
- Data quality evaluation in wearable monitoring.Scientific reports · 2022Article
Corrections and comments
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Authors and funding
12 authors.
Funding
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.
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