Automated Detection of Rest Disruptions in Critically Ill Patients
This addresses the challenge of monitoring sleep quality in critically ill patients, which is incremental as it applies an existing computer vision method to a new medical dataset.
The study tackled the problem of automatically detecting visitation frequency in ICU patients to assess sleep disruptions, finding that frequent interruptions were associated with increased pain and longer hospital stays.
Sleep has been shown to be an indispensable and important component of patients recovery process. Nonetheless, sleep quality of patients in the Intensive Care Unit (ICU) is often low, due to factors such as noise, pain, and frequent nursing care activities. Frequent sleep disruptions by the medical staff and/or visitors at certain times might lead to disruption of patient sleep-wake cycle and can also impact the severity of pain. Examining the association between sleep quality and frequent visitation has been difficult, due to lack of automated methods for visitation detection. In this study, we recruited 38 patients to automatically assess visitation frequency from captured video frames. We used the DensePose R-CNN (ResNet-101) model to calculate the number of people in the room in a video frame. We examined when patients are interrupted the most, and we examined the association between frequent disruptions and patient outcomes on pain and length of stay.