Sonali Santhosh
Class of 2026Richland, WA
About
Hello! My name is Sonali Santhosh and I decided to work on this project because I’m deeply fascinated by the brain, especially seizures, and how they affect our most vulnerable patients. As someone who aspires to become a neurosurgeon in the future, I’m particularly interested in understanding and addressing critical neurological conditions such as seizures. This project combines my passion for neuroscience with my goal of making an impact on patient care, and helped grow my understanding of machine learning.Projects
- "How effective are Recurrent Neural Networks (RNNs) in predicting the onset of seizure activity in neonates using EEG data, and can they outperform traditional methods in terms of early detection time?" with mentor David (Working project)
Project Portfolio
How effective are Recurrent Neural Networks (RNNs) in predicting the onset of seizure activity in neonates using EEG data, and can they outperform traditional methods in terms of early detection time?
Started Mar. 29, 2024
Abstract or project description
This project explores the effectiveness of Recurrent Neural Networks (RNNs) in predicting the onset of seizures in neonates using EEG data. Given the critical need for timely seizure detection in neonatal intensive care units, traditional methods, which often rely on expert analysis, can be slow and less effective in early detection. By utilizing a dataset of neonatal EEG recordings with seizure annotations, and applying advanced RNN models to analyze the data, the goal is to determine whether RNNs can provide more accurate and faster predictions of seizure activity compared to traditional techniques. By leveraging RNNs, the project aims to enhance early intervention strategies, potentially reducing the long-term impacts of neonatal seizures and improving outcomes for vulnerable neonates.