Acoustic monitoring of lung sounds for the detection of one lung intubation

Abstract

Patient monitoring methods for the early diagnosis of one lung intubation (OLI) are non-specific and controversial. The aim of this study is to evaluate a new acoustic monitoring system for the detection of OLI. Lung sounds were collected from 24 adult surgical patients scheduled for routine surgical procedures. Four piezoelectric microphones attached to the patients' back were used to sample lung sounds during induction to anesthesia and tube positioning. To achieve OLI, the endotracheal tube was inserted and advanced down the airway so that diminished or no breath sounds were heard on the left side of the chest. The tube was then withdrawn stepwise until equal breath sounds were heard. Fiberoptic bronchoscopy confirmed the tube's final position. Acoustic analyses were preformed by a new algorithm which assumes a Multiple Input Multiple Output (MIMO) system, in which a multi-dimensional Auto-Regressive (AR) model relates the input (lungs) and the output (recorded sounds) and a classifier, based on a Generalized Likelihood Ratio Test (GLRT), indicates the number of ventilated lungs without retrieving the original lung sounds from the recorded samples. This algorithm achieved an OLI detection probability of 95.2% with a false alarm probability of 4.8%. Higher detection values can be achieved at the price of a higher incidence of false alarms.