PURPOSE: The aim of this study is to assess how analysis of the spectral characteristics of lung sounds can help in detecting lung injury and subsequent recruitment as verified by computed tomography (CT). METHODS: Lung sounds were recorded at four locations (ventral and dorsal on right and left side) in six ventilated pigs before and after unilateral oleic acid-induced lung injury during sequential increase of positive end-expiratory pressure (PEEP) from 0 to 20 cmH(2)O. CT scans of the chest were used for comparison with lung aeration. Sound characteristics were compared by computer-aided analysis in the time and frequency domain as well as by clinicians. RESULTS: The presence of lung injury and its location were detected by substantial acoustic spectral components above 500 Hz (frequency) and -70 dB (amplitude). Application of increasing PEEP gradually reduced the pathological components as CT analysis verified recruitment. At 20 cmH(2)O PEEP there was no further tidal recruitment of injured lung and the pathological sounds had disappeared, rendering the lung sounds of the injured lung similar to those of the control lung. This was mirrored by the clinicians' characterization of the sounds. CONCLUSIONS: Computer-aided analysis of lung sounds is suitable for detection of pathological lung sounds and may guide in detection and recruitment of poorly/nonaerated lung.
PURPOSE: The aim of this study is to assess how analysis of the spectral characteristics of lung sounds can help in detecting lung injury and subsequent recruitment as verified by computed tomography (CT). METHODS: Lung sounds were recorded at four locations (ventral and dorsal on right and left side) in six ventilated pigs before and after unilateral oleic acid-induced lung injury during sequential increase of positive end-expiratory pressure (PEEP) from 0 to 20 cmH(2)O. CT scans of the chest were used for comparison with lung aeration. Sound characteristics were compared by computer-aided analysis in the time and frequency domain as well as by clinicians. RESULTS: The presence of lung injury and its location were detected by substantial acoustic spectral components above 500 Hz (frequency) and -70 dB (amplitude). Application of increasing PEEP gradually reduced the pathological components as CT analysis verified recruitment. At 20 cmH(2)O PEEP there was no further tidal recruitment of injured lung and the pathological sounds had disappeared, rendering the lung sounds of the injured lung similar to those of the control lung. This was mirrored by the clinicians' characterization of the sounds. CONCLUSIONS: Computer-aided analysis of lung sounds is suitable for detection of pathological lung sounds and may guide in detection and recruitment of poorly/nonaerated lung.
Authors: Adriano M Alencar; Stephen P Arold; Sergey V Buldyrev; Arnab Majumdar; Dimitrije Stamenović; H Eugene Stanley; Béla Suki Journal: Nature Date: 2002-06-20 Impact factor: 49.962
Authors: Raymond L H Murphy; Andrey Vyshedskiy; Verna-Ann Power-Charnitsky; Dhirendra S Bana; Patricia M Marinelli; Anna Wong-Tse; Rozanne Paciej Journal: Respir Care Date: 2004-12 Impact factor: 2.258
Authors: Massimo Antonelli; Marc Bonten; Jean Chastre; Giuseppe Citerio; Giorgio Conti; J Randall Curtis; Daniel De Backer; Goran Hedenstierna; Michael Joannidis; Duncan Macrae; Jordi Mancebo; Salvatore M Maggiore; Alexandre Mebazaa; Jean-Charles Preiser; Patricia Rocco; Jean-François Timsit; Jan Wernerman; Haibo Zhang Journal: Intensive Care Med Date: 2012-01-04 Impact factor: 17.440