Detection of one lung intubation by monitoring lungs sounds

Analysis of lungs sounds for monitoring and diagnosis of pulmonary function is well known. One of the applications of this method is detection of one lung intubation (OLI) during anesthesia or intensive care. An algorithm for detection the one-lung ventilation situation from the lungs sounds is presented. The algorithm assumes a MIMO (Multiple Input Multiple Output) system, in which a multidimensional AR (Auto-Regressive) model relates the input (lungs) and the output (recorded sounds). The unknown AR parameters are estimated, and a detector based on the estimated eigenvalues of the source covariance matrix is developed, in order to detect one lung ventilation situation. Testing the algorithm on real breathing sounds, which were recorded in a surgery room, shows more than 90% accuracy in OLI detection.