  In a clinical setting, epilepsy patients are monitored via video
electroencephalogram (EEG) tests. A video EEG records what the patient
experiences on videotape while an EEG device records their brainwaves.
Currently, there are no existing automated methods for tracking the patient's
location during a seizure, and video recordings of hospital patients are
substantially different from publicly available video benchmark datasets. For
example, the camera angle can be unusual, and patients can be partially covered
with bedding sheets and electrode sets. Being able to track a patient in
real-time with video EEG would be a promising innovation towards improving the
quality of healthcare. Specifically, an automated patient detection system
could supplement clinical oversight and reduce the resource-intensive efforts
of nurses and doctors who need to continuously monitor patients. We evaluate an
ImageNet pre-trained Mask R-CNN, a standard deep learning model for object
detection, on the task of patient detection using our own curated dataset of 45
videos of hospital patients. The dataset was aggregated and curated for this
work. We show that without fine-tuning, ImageNet pre-trained Mask R-CNN models
perform poorly on such data. By fine-tuning the models with a subset of our
dataset, we observe a substantial improvement in patient detection performance,
with a mean average precision of 0.64. We show that the results vary
substantially depending on the video clip.
