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Epilepsy is a neurological disease that causes recurrent, spontaneous seizures, which can lead people to experience ephemeral neurological and physiological impairments and disrupt day-to-day living. One of the most enervating facets of epilepsy is the unpredictability of seizures. Most people reside in fear, stress, and anxiety of not knowing when a seizure might occur, which in turn can serve as a major disability and cause people to encounter difficulties engaging in daily activities. For decades, many seizure prediction studies have concentrated on utilizing long term electroencephalography (EEG) data from continuous scalp or intracranial EEG electrode monitoring. While these studies have shown positive results for seizure predictive capabilities, continuous EEG electrode monitoring can be invasive, uncomfortable, and pose some potential risk for patient with epilepsy. Thus, seizure prediction remains a significant challenge within epilepsy-based research.In efforts to advance seizure prediction, this dissertation work applies both quantitative and machine learning methods to overcome these challenges. In examining patient-specific seizure diaries that consist of possible seizure predictive factors (e.g., measurements of mood, stress, and circadian patterns), the first method focuses on using generalized linear modeling, specifically logistic regression, to predict subsequent seizures within a 24-hour timeframe. Following, predictive factors were used to generate quantitative biomarkers that associated with seizure occurrences and were analyzed via diagnostic tests. The second method focused on using a machine learning technique, specifically decision trees, to showcase how possible predictive factors are associated with seizure outcome. Additionally, certain factors were categorized into groups based on frequently they appeared in patients’ decision trees. The significance of these seizure predictive factors was also identified.This dissertation work shows that by optimizing individual medical reports, as well as quantitative and machine learning techniques, designing individualized regimen for patients with epilepsy is possible. The research methods carried out in this work aimed to bridge the connection between seizure prediction and exploring possible triggers that influence seizure onset. |
