title: Interpretation of spirometry through signal analysis.
abstract: Chronic obstructive pulmonary disease is attaining alarming proportions that requires more objective and quantitative ways for the diagnosis and the evaluation / stratification of, both, the disease and the therapeutic outcomes. Within this context, the present study explores the possibility to increase the effectiveness of spirometry through signal analysis. Expiratory flow results from converging airflows at different levels of airway branching. Furthermore, along a branching network of air conduits, the characteristics of converging air currents determine those of the resulting air flow. Thus, for the human bronchial tree, the characteristics of air currents within the smaller branches are, ideally, conserved at the expiratory flow recorded at the mouth. This makes it theoretically possible to use signal analysis methodologies in order to identify the characteristics of airflow along the different levels of the respiratory tree. The present study reports on an attempt to identify alterations non-invasively in the frequency spectrum of the first derivative of the Forced Vital Capacity curve of patients presenting with different respiratory conditions. Such alterations can be attributed to the onset and operation of the airway closure phenomenon that limits airflow, during forced expiration. Fundamental to the design of the study was the notion that the forced expiratory output of the respiratory system is determined by the bronchial tree and the upper respiratory tract. These two entities shape the air flow that is expelled from the collective airspace of the bronchial tree subdivisions distal to the terminal bronchi. At the end we were able to identify simple measures that are derived from the power spectrum of the derivative of the spirometric curve that permit the definition of specific filters and allow for the accurate classification of, at least, the basic types of respiratory disease.
authors.affiliation: Department of Physiology, Medical University of Varna, Bulgaria. antonia@med.auth.gr
authors.forename: Antonia
authors.initials: A
authors.lastname: Anogeianaki
mesh_terms: D003627:Data Interpretation, Statistical; D006801:Humans; D029424:Pulmonary Disease, Chronic Obstructive / Q000503:physiopathology; D013147:Spirometry