Interpretation of spirometry through signal analysis.

Area of Science:

• Pulmonary Medicine
• Biomedical Engineering
• Signal Processing

Background:

• Chronic obstructive pulmonary disease (COPD) prevalence necessitates improved diagnostic and stratification methods.
• Current spirometry lacks sufficient objectivity and quantification for disease and treatment evaluation.
• Airflow dynamics within the bronchial tree influence expiratory flow, offering potential for signal analysis.

Purpose of the Study:

• To enhance spirometry's diagnostic effectiveness through advanced signal analysis techniques.
• To explore non-invasive methods for identifying respiratory alterations using airflow characteristics.
• To develop quantitative measures for classifying respiratory diseases based on spirometry data.

Main Methods:

• Analysis of the frequency spectrum of the first derivative of the Forced Vital Capacity (FVC) curve.
• Utilizing signal analysis methodologies to interpret airflow patterns within the respiratory tree.
• Investigating the relationship between airflow characteristics and airway closure phenomena.

Main Results:

• Identified alterations in the frequency spectrum related to airway closure during forced expiration.
• Demonstrated that the bronchial tree and upper respiratory tract shape expiratory airflow.
• Developed simple measures from the spirometric curve's derivative power spectrum for disease classification.

Conclusions:

• Signal analysis of spirometry, specifically the FVC curve derivative, offers objective diagnostic capabilities.
• The power spectrum analysis allows for the definition of filters to accurately classify respiratory diseases.
• This approach provides a quantitative method for evaluating respiratory conditions and therapeutic outcomes.