Reckoning of facial grimacing scheme for patient agitation in critical care

Abstract

Reckoning patient grimacing in Intensive Care Unit (ICU) is a crucial demand in checking routine procedure. Despite of detecting any physical change in the body of the patient, medical staff has to go up and down to the patient bed several times and to report to surgeons accurately. By viewing the patient grimacing changes, the surgeons can get full details of the way of changes in physical body; certainly this cannot be explained accurately by the nurses. As such, this series of research is focus on real-time measurement of high resolution facial changes that are observed to occur in agitation. An algorithm is developed that measures the degree of facial grimacing from a single digital camera. The method is demonstrated on simulated patient facial motion to prove the concept. A consistent measure is obtained that is robust to significant random angle head movement and compares well against visual observation of different levels of grimacing. The method provides a basis for clinical validation. Finally, the conditions of the patient were determined based on classifiers such as K-Nearest Neighborhood (k-NN), Fuzzy k-NN (f-kNN), Linear Discriminant Analysis (LDA) and Neural Network (NN).