Feasibility of Regression Modeling and Biomarker Analysis for Epileptic Seizure Prediction

Abstract

Epilepsy is a neurological disease that causes recurrent, spontaneous seizures, which can lead people to experience ephemeral neurological and physiological impairments that disrupt day-to-day living. To advance seizure prediction, this study focused on the feasibility of self-prediction by examining patient-specific morning and evening seizure diaries that consisted of possible seizure triggers, measurements of mood, and predictive symptoms. Prediction models were generated by employing logistic regression. Akaike Information Criterion was used to select ideal regression models that evaluated patients' data. Biomarkers that were associated with seizure occurrences calculated and analyzed. Seizure prediction model performance accuracy varied among patients. The correlation between seizure occurrences and how biomarkers oscillated over time was identified. This research expanded efforts to further improve precision medicine and build more steadfast epilepsy-based healthcare treatments.