Online evaluation of a changes detection algorithm for depth of anesthesia signals

Abstract

The detection of changes in the signals used to evaluate the depth of anesthesia of patients undergoing surgery is of foremost importance. This detection allows to decide how to adapt the doses of hypnotics and analgesics to be administered to patients for minimally invasive diagnostics and therapeutic procedures. This paper presents an algorithm based on the Page-Hinkley test to automatically detect changes in the referred depth of anesthesia signals of patients undergoing general anesthesia. The performance of the proposed method is evaluated online using data from patients subject to surgery. The results show that most of the detected changes are in accordance with the actions of the clinicians in terms of times where a change in the hypnotic or analgesic rates had occurred. This detection was performed under the presence of noise and sensor faults. The results encourage the inclusion of the proposed algorithm in a decision support system based on depth of anesthesia signals.