Selection of Low‐Density EEG Montages for Optimal Seizure Detection Performance
6 affiliations
- 1Usher Institute College of Medicine and Veterinary Medicine University of Edinburgh Edinburgh UK
- 2Institute for Imaging, Data and Communications School of Engineering University of Edinburgh Edinburgh UK
- 3Combined Paediatric Neuroscience Service Royal Hospital for Children and Young People Edinburgh UK
- 4BrainsView Inc. Ontario Canada
- 5Muir Maxwell Epilepsy Centre University of Edinburgh Edinburgh UK
- 6Paediatric Critical Care Unit Royal Hospital for Children and Young People Edinburgh UK
Abstract
Multi‐channel electroencephalography (EEG) is widely recognised for its ability to non‐invasively capture dynamic brain activity with high temporal resolution. However, conventional EEG systems require meticulous setup, expert interpretation and specialised infrastructure, which limits their applicability in real‐time, resource‐constrained environments such as critical care and emergency settings. As wearable and point‐of‐care EEG technologies gain traction, there is increasing demand for low‐density, automated seizure detection methods that are clinically reliable and scalable. This study presents a data‐driven pipeline for an EEG‐based task, seizure detection, that identifies compact and clinically relevant four‐channel electrode configurations. Using standard 10–20 EEG recordings, the pipeline integrates clinical domain knowledge, preprocessing, feature extraction, and ensemble boosted classification to evaluate seizure detection performance across multiple electrode combinations. The approach was validated on both a heterogeneous local paediatric EEG dataset and an open‐source adult dataset. Our results show that several four‐channel montages achieve robust patient‐independent performance, with central and temporal electrodes frequently appearing in the top‐performing configurations. These findings support the feasibility of accurate seizure detection using low‐density EEG, even across diverse clinical populations. The primary contribution of this work is a generalisable pipeline for selecting compact EEG montages, which balances domain insight and data‐driven optimisation. This framework can be readily adapted to other EEG‐based tasks and offers a practical path toward real‐time wearable EEG monitoring in resource‐constrained environments.
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BibTeX
@article{abdullateef2026selection,
title = {{Selection of Low‐Density EEG Montages for Optimal Seizure Detection Performance}},
author = {Shima Abdullateef and Iris Soare‐Nguyen and Brian Jordan and Ailsa McLellan and Vera Nenadovic and Javier Escudero and Milly Lo},
journal = {Healthcare Technology Letters},
year = {2026},
month = oct,
doi = {10.1049/htl2.70104},
url = {https://doi.org/10.1049/htl2.70104},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Selection of Low‐Density EEG Montages for Optimal Seizure Detection Performance
AU - Shima Abdullateef
AU - Iris Soare‐Nguyen
AU - Brian Jordan
AU - Ailsa McLellan
AU - Vera Nenadovic
AU - Javier Escudero
AU - Milly Lo
PY - 2026
DA - 2026/10/26
JO - Healthcare Technology Letters
DO - 10.1049/htl2.70104
UR - https://doi.org/10.1049/htl2.70104
AB - Multi‐channel electroencephalography (EEG) is widely recognised for its ability to non‐invasively capture dynamic brain activity with high temporal resolution. However, conventional EEG systems require meticulous setup, expert interpretation and specialised infrastructure, which limits their applicability in real‐time, resource‐constrained environments such as critical care and emergency settings. As wearable and point‐of‐care EEG technologies gain traction, there is increasing demand for low‐density, automated seizure detection methods that are clinically reliable and scalable. This study presents a data‐driven pipeline for an EEG‐based task, seizure detection, that identifies compact and clinically relevant four‐channel electrode configurations. Using standard 10–20 EEG recordings, the pipeline integrates clinical domain knowledge, preprocessing, feature extraction, and ensemble boosted classification to evaluate seizure detection performance across multiple electrode combinations. The approach was validated on both a heterogeneous local paediatric EEG dataset and an open‐source adult dataset. Our results show that several four‐channel montages achieve robust patient‐independent performance, with central and temporal electrodes frequently appearing in the top‐performing configurations. These findings support the feasibility of accurate seizure detection using low‐density EEG, even across diverse clinical populations. The primary contribution of this work is a generalisable pipeline for selecting compact EEG montages, which balances domain insight and data‐driven optimisation. This framework can be readily adapted to other EEG‐based tasks and offers a practical path toward real‐time wearable EEG monitoring in resource‐constrained environments.
SN - 2053-3713
ER -