Epilepsy affects over 50 million worldwide, yet effective disease monitoring methods are scarce and limited. Our research aims to tackle the issue of detecting epilepsy and seizure events as well as assessing the severity of epilepsy and the effects of different epilepsy treatments. To accurately forecast seizures, we will use EEG and breathing data to create a self-supervised learning model that can classify and predict when patients are approaching a state of seizure. To detect post-seizure apnea events, we will implement a prediction model that can classify when a patient has entered a state of apnea. To assess the severity of epilepsy and different treatments, we will explore the data of control patients against the data of patients with treatment to suggest possible conclusions.

I am participating in the SuperUROP program because I am very interested in leveraging my technical background in the medical research field. While I have learned a lot in my coursework, I am excited to apply my skills and to a real-world application. Through this experience, I hope to deepen my understanding of machine learning and contribute to an impactful research project.