• Project Title: Applying Computer Vision Models for Seizure Patient Tracking and Out-Of-Distribution Video EEG Detection

  • BASIS Advisor: Swetha Bhattacharya

  • Internship Location: Stanford Medicine

  • Onsite Mentor: Dr. Daniel Rubin, Dr. Christopher Lee-Messer

Currently, in a clinical setting, epilepsy patients are monitored via video electroencephalogram (EEG) tests. A video EEG records what the patient experiences on video tape while an EEG device records his or her brainwaves. While patients are typically monitored at all times, epileptic seizures are unpredictable, and it may take time for nurses to respond to the patient. While epileptic seizures are dangerous, patients may also be harmed through other events like a fall or choking. Moreover, there are currently no non-invasive methods for tracking the patient’s location during a seizure. Being able to track a patient in real-time with video EEGwould be a promising innovation towards improving the quality of healthcare. I propose using state-of-the-art object detection models like Detectron2 combined with an out-of-distribution (OOD) approach to track patients in the ICU and find anomalies in their movements.