Biomedical Engineering - Theses
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ItemNovel seizure risk markers( 2022)Epilepsy is one of the most common severe neurological diseases and is characterized by recurring seizures. Currently, about 70 million people worldwide live with epilepsy and over 30% of them cannot be adequately treated with medication. The unpredictability of seizures is a severely debilitating aspect of epilepsy that significantly impacts the quality of life of patients. Consequently, there is a clinical need to find new markers that are useful for seizure forecasting. High-frequency activity (HFA) is a newly proposed biomarker for epilepsy, but its predictive value in seizure forecasting remains uncertain. Emerging new evidence has shown that ambient air pollution affects the central nervous system, but little is known about its effect on epileptic seizures. The goal of this thesis is to investigate potential novel markers for improving seizure forecasting and epilepsy management. To achieve this goal, the work of this thesis addresses several key questions: How do HFA rates and locations change over time, and how do these changes correspond with seizures? Can HFA forecast seizures? Is ambient air pollution associated with the risk of epileptic seizures? By addressing these fundamental questions, this thesis aims to provide the basis for formulating an innovative approach to improve seizure forecasting and control. In the first research chapter, the spatiotemporal profiles of HFA are investigated using long-term intracranial EEG. The results show that HFA rates have post-implantation variability, periodic cycles, and patient-specific relationships with seizures. These findings caution against using HFA as a presurgical metric without testing its reliability over time and suggest that tracking and utilising cycles of HFA rates may offer an exciting new opportunity to track cycles of seizures. In the second research chapter, a real-time phase estimation approach and seizure forecasting framework are developed using instantaneous HFA rates and phases of HFA cycles. The results show that HFA can be a useful biomarker to forecast seizures in patients with refractory epilepsy. The proposed real-time phase estimation approach can estimate the HFA phase over time with high accuracy and can be generalized to other seizure risk markers. In the third research chapter, ambient air pollutants are explored as potential seizure risk factors using a participant-time-stratified case-crossover design with conditional Poisson regression models. The results show that elevated ambient carbon monoxide (CO) concentrations, though within the Australian air quality standard, may be associated with increased risks of epileptic seizures; no significant associations were found in the other studied air pollutants (nitrogen dioxide, ozone, sulphur dioxide, and particulate matter less than 10 micrometers in diameter). These findings may have important clinical and public health implications and may offer potential new leads to improve seizure forecasting and prevention. Overall, these studies provide new evidence that HFA and ambient CO may serve as potential novel seizure risk markers. It is our hope that the work of this thesis contributes towards real-life seizure forecasting, informing new strategies to reduce the uncertainty of seizures, and eventually improving epilepsy management and the quality of life for people with epilepsy.