Seizure detection with reduced electroencephalogram channels: research trends and outlook

Christina Maher; Yikai Yang; Nhan Duy Truong; Chenyu Wang; Armin Nikpour; Omid Kavehei

doi:10.1098/rsos.230022

Seizure detection with reduced electroencephalogram channels: research trends and outlook

R Soc Open Sci. 2023 May 3;10(5):230022. doi: 10.1098/rsos.230022. eCollection 2023 May.

Authors

Christina Maher^{1

2}, Yikai Yang¹, Nhan Duy Truong¹, Chenyu Wang^{2

3

4}, Armin Nikpour^{2

5

6}, Omid Kavehei¹

Affiliations

¹ School of Biomedical Engineering, Faculty of Engineering, The University of Sydney, Sydney, New South Wales 2006, Australia.
² Brain and Mind Centre, The University of Sydney, Sydney, New South Wales 2006, Australia.
³ Translational Research Collective, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales 2050, Australia.
⁴ Sydney Neuroimaging Analysis Centre, Camperdown, New South Wales 2050, Australia.
⁵ Central Clinical School, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales 2006, Australia.
⁶ Translational Research Collective, Faculty of Medicine and Health, The University of Sydney, Camperdown, New South Wales 2050, Australia.

Abstract

Epilepsy is a prevalent condition characterized by recurrent, unpredictable seizures. Monitoring with surface electroencephalography (EEG) is the gold standard for diagnosing epilepsy, but a time-consuming, uncomfortable and sometimes ineffective process for patients. Further, using EEG over a brief monitoring period has variable success, dependent on patient tolerance and seizure frequency. The availability of hospital resources and hardware and software specifications inherently restrict the options for comfortable, long-term data collection, resulting in limited data for training machine-learning models. This mini-review examines the current patient journey, providing an overview of the current state of EEG monitoring with reduced electrodes and automated channel reduction methods. Opportunities for improving data reliability through multi-modal data fusion are suggested. We assert the need for further research in electrode reduction to advance brain monitoring solutions towards portable, reliable devices that simultaneously offer patient comfort, perform ultra-long-term monitoring and expedite the diagnosis process.

Keywords: electrode; electroencephalogram; epilepsy; machine learning; patient care; seizure detection.

Publication types

Review