Contributions of signal analysis to the interpretation of spirometry.

AIM: Chronic Obstructive Pulmonary Disease (COPD) is taking on catastrophic proportions. However, there is still a need for more objective and quantitative methods for its diagnosis and stratification. The present study explores the effectiveness of signal analysis methodologies as the means to increase the effectiveness of spirometry in diagnosing and stratifying COPD.
METHODS: Since expiratory flow at the mouth results from converging airflows, it is possible to use signal analysis to identify changes in the characteristics of airflow along the respiratory tree. This was achieved by non-invasively identifying alterations in the frequency spectrum of the Forced Expiratory Flow (FEF) curve of 108 patients (49 men and 59 women, 12-75 yrs of age) presenting with (a) clinically and spirometrically normal respiratory profile, (b) COPD, (c) restrictive lung disease and (d) interstitial fibrosis. Fundamental to the study design was the notion that the characteristics of the expiratory output of the respiratory system are determined by the bronchial tree and the upper respiratory tract.
RESULTS: A number of quantitative measures for the power spectrum of the FEF curve were identified, which permit the definition of specific rules and allow for the accurate classification of, at least, the basic types of respiratory disease.
CONCLUSIONS: (a) It is for the first time that airflow resonances are identified in the sub-audible (<20 Hz) range of the power spectrum of the FEF curve. (b) COPD patients present with FEF curves which have different power spectral characteristics from those of healthy individuals (p<0.01), at frequencies lower than 3.66 Hz. (c) In COPD, in restrictive lung disease and in interstitial fibrosis, the lower resonant frequencies of the spectrum of the FEF curve predominate.