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.
Highlights
There has been great interest recently in identifying biomarkers for seizure susceptibility by looking into critical transitions in brain dynamics in order to enhance the precision of seizure forecasting in a cohort of patients with focal epilepsy [1,2,3]
Chronic and often intracranial electroencephalogram (EEG) recordings demonstrated some limited evidence of circadian, multidien, and circannual cycles in epileptic brain dynamics [4,5,6]
We propose a framework to minimize the risk of sudden unexpected death in epilepsy (SUDEP), especially for patients with uncontrolled epilepsy
Summary
There has been great interest recently in identifying biomarkers for seizure susceptibility by looking into critical transitions in brain dynamics in order to enhance the precision of seizure forecasting in a cohort of patients with focal epilepsy [1,2,3]. These studies often require a very long recording that is not available and, are critically lacking. These studies can be grouped into two categories: [1] finding discriminative features with various signal processing and transformation techniques and [2] leveraging deep learning’s
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