Detection of one-lung intubation incidents.

Abstract

Lung sounds are very common source for monitoring and diagnosis of pulmonary function. This approach can be used for detecting one lung intubation (OLI) during anesthesia or intensive care. In this paper, an algorithm for detecting OLI from lung sounds is presented. The algorithm assumes a multiple-input-multiple-output system, in which a multi-dimensional auto-regressive model relates the input (lungs) and the output (recorded sounds). An OLI detector is developed based on the generalized likelihood ratio test (GLRT), assuming coherent distributed sources for each lung. This method exhibited reliable results also when the lungs were modeled by incoherent distributed sources, which is a more accurate model for lung sources. The algorithm was tested using real breathing sounds recorded in an operating room, and it achieved an OLI detection rate of more than 95%, for each breathing cycle.