Automated Detection of Patients in Hospital Video Recordings

TL;DR

This paper explores the use of deep learning, specifically Mask R-CNN, for automated patient detection in hospital video recordings, addressing challenges posed by unique clinical environments and limited existing datasets.

Contribution

It demonstrates the effectiveness of fine-tuning pre-trained Mask R-CNN models on a curated hospital video dataset for improved patient detection accuracy.

Findings

Fine-tuning improves detection performance significantly.

Model performance varies across different video clips.

Pre-trained models perform poorly without fine-tuning.

Abstract

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.