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

INTRODUCTION: Monitoring methods for the early diagnosis of one-lung intubation (OLI) are nonspecific and controversial. In this study, we evaluated a new acoustic monitoring system for the detection of OLI. METHODS: Lung sounds were collected from 24 adult surgical patients scheduled for routine surgical procedures. Four piezoelectric microphones attached to the patients' backs were used to sample lung sounds during induction of anesthesia and endotracheal 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 The tube was then withdrawn stepwise until equal breath sounds were heard.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 system, in which a multidimensional Auto-Regressive model relates the input (lungs) and the output (recorded sounds) and a classifier, based on a Generalized Likelihood Ratio Test, indicates the number of ventilated lungs without reconstructing the original lung sounds from the recorded samples. RESULTS: This algorithm achieved an OLI detection probability of 95.2% with a false alarm probability of 4.8%. CONCLUSION: Higher detection values can be achieved at the price of a higher incidence of false alarms.

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.

Underwater source detection using a spatial stationarity test.

The problem of detecting a source in shallow water is addressed. The complexity of such a propagation channel makes precise modeling practically impossible. This lack of accuracy causes a deterioration in the performance of the optimal detector and motivates the search for suboptimal detectors which are insensitive to uncertainties in the propagation model. A novel, robust detector which measures the degree of spatial stationarity of a received field is presented. It exploits the fact that a signal propagating in a bounded channel induces spatial nonstationarity to a higher degree than mere background noise. The performance of the proposed detector is evaluated using both simulated data and experimental data collected in the Mediterranean Sea. This performance is compared to those of three other detectors, employing different extents of prior information. It is shown that when the propagation channel is not completely known, as is the case of the experimental data, the novel detector outperforms the others in terms of threshold signal-to-noise ratio (SNR). In the presence of environmental mismatch, the threshold SNR of the novel detector for the experimental data appears 2-5 dB lower than the other detectors. That is, this detector couples good performance with robustness to propagation uncertainties.