Detection of One Lung Intubation by monitoring lungs sounds

L. Weizman, J. Tabrikian, A. Cohen

Research output: Contribution to journalConference articlepeer-review

6 Scopus citations

Abstract

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. In this paper, 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 multi-dimensional 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.

Original languageEnglish
Pages (from-to)917-920
Number of pages4
JournalAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume26 II
StatePublished - 1 Dec 2004
EventConference Proceedings - 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2004 - San Francisco, CA, United States
Duration: 1 Sep 20045 Sep 2004

Keywords

  • AR
  • LUNGS
  • MIMO
  • OLI

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