Pediatric Seizure Detection System: A Data-Driven Approach with Predictive and Generative AI Integration

The gravity of seizures in pediatric populations cannot be overstated. From the youngest infants to adolescents on the brink of adulthood, seizures disrupt not only neurological functions but also the fabric of daily life. According to the Centers for Disease Control and Prevention, about 470,000 children in the United States have epilepsy, which is a neurological disorder characterized by recurrent seizures. Drawing from personal encounters with seizures, I recognize the critical need for effective seizure detection mechanisms. This study addresses the challenge of detecting seizures in children, especially in environments where constant supervision may not be feasible. Utilizing CAD, 3D design, and machine learning algorithms, this prototype predicts a seizure onset by analyzing bio data from a simulation of possible data sets carefully overviewed by my pediatrician Sara MachMan. The prototype integrates insights from a literature search, including the CDC publication on seizure detection, highlighting brain activity, fever, and body movements as triggers. Based on these triggers and thorough data analysis with a pediatrician, I was able to discover a breakthrough. Despite encountering a 15% error rate from my simulated data analysis, the prototype effectively alerts parents and medical professionals, demonstrating its potential to reduce the unpredictability of seizures and positively impact lives.