Finding the lung sound-flow relationship in normal and asthmatic subjects.
To investigate the relationship between lung sound (LS) and flow, we studied LS signals from 5 healthy adults (group I), 10 healthy children (group II) and 7 asthmatic children (group III). The LS signals were recorded on right upper lung lobe at different flow rates varied from 0.4 to 3.0 L/s and the flow signals were measured at mouth. The LS and flow signals were parsed into segments of 1024 data points with 50% overlap between successive segments. The mean LS amplitude (mean AMP) and mean flow (flow) were calculated for each segment. The average power (Pave) of each segment was calculated from LS spectrum for different frequency bands between 20-600 Hz. Four different types of models, representing the relationship between mean AMP or Pave and flow, were investigated using different percentage of flow signal in each inspiratory phase. The model coefficients were derived from either linear regression analysis or polynomial curve fitting between the data and model variables. The correlation coefficients (r) between the experimental data and data estimated from the model coefficients were calculated for each subject in each model and averaged between the subjects. The results showed much stronger correlation between Pave and flow than mean AMP and flow for all groups. The best model to describe Pave relationship with flow was found to be power relationship in both healthy adults and children whereas a third-order polynomial curve best fitted the Pave and flow data in asthmatic group. The optimum frequency band to calculate Pave was found to be 150-450 Hz for healthy subjects and 300-600 Hz for asthmatic children. The diminution of heart sound (HS) from LS recordings showed no change in the selected model in all three groups. The results of this study suggest the difference in Pave- flow relationship in healthy and asthmatic subjects may be used as a diagnostic tool for asthma.
Hossain
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I
Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, MB. ihossain@ee.umanitoba.ca
Moussavi
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Z