Seizure Susceptibility Prediction in Uncontrolled Epilepsy

Nhan Duy Truong; Yikai Yang; Christina Maher; Levin Kuhlmann; Alistair McEwan; Armin Nikpour; Omid Kavehei

doi:10.3389/fneur.2021.721491

Seizure Susceptibility Prediction in Uncontrolled Epilepsy

Front Neurol. 2021 Sep 13:12:721491. doi: 10.3389/fneur.2021.721491. eCollection 2021.

Authors

Nhan Duy Truong^{1

2}, Yikai Yang¹, Christina Maher¹, Levin Kuhlmann^{3

4}, Alistair McEwan¹, Armin Nikpour^{5

6}, Omid Kavehei^{1

2}

Affiliations

¹ Australian Research Council Training Centre for Innovative BioEngineering, School of Biomedical Engineering, Faculty of Engineering, The University of Sydney, Sydney, NSW, Australia.
² The University of Sydney Nano Institute, Sydney, NSW, Australia.
³ Faculty of Information Technology, Monash University, Melbourne, VIC, Australia.
⁴ Department of Medicine - St. Vincent's Hospital Melbourne, The University of Melbourne, Fitzroy, VIC, Australia.
⁵ Comprehensive Epilepsy Service and Department of Neurology at the Royal Prince Alfred Hospital, Sydney, NSW, Australia.
⁶ Faculty of Medicine and Health, Central Clinical School, The University of Sydney, Sydney, NSW, Australia.

Abstract

Epileptic seizure forecasting, combined with the delivery of preventative therapies, holds the potential to greatly improve the quality of life for epilepsy patients and their caregivers. Forecasting seizures could prevent some potentially catastrophic consequences such as injury and death in addition to several potential clinical benefits it may provide for patient care in hospitals. The challenge of seizure forecasting lies within the seemingly unpredictable transitions of brain dynamics into the ictal state. The main body of computational research on determining seizure risk has been focused solely on prediction algorithms, which involves a challenging issue of balancing sensitivity and false alarms. There have been some studies on identifying potential biomarkers for seizure forecasting; however, the questions of "What are the true biomarkers for seizure prediction" or even "Is there a valid biomarker for seizure prediction?" are yet to be fully answered. In this paper, we introduce a tool to facilitate the exploration of the potential biomarkers. We confirm using our tool that interictal slowing activities are a promising biomarker for epileptic seizure susceptibility prediction.

Keywords: Bayesian; epileptic seizure forecasting; probabilistic programming; uncertainty level; variational inference.